BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, Shilton A, Yearwood J, Dimitrova N, Ho TB, Venkatesh S, Berk M. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View. J Med Internet Res 2016;18:e323. [PMID: 27986644 DOI: 10.2196/jmir.5870] [Cited by in Crossref: 366] [Cited by in F6Publishing: 382] [Article Influence: 52.3] [Reference Citation Analysis]
Number Citing Articles
1 Gleeson F, Revel MP, Biederer J, Larici AR, Martini K, Frauenfelder T, Screaton N, Prosch H, Snoeckx A, Sverzellati N, Ghaye B, Parkar AP. Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI). Eur Radiol 2023. [PMID: 36729173 DOI: 10.1007/s00330-023-09409-2] [Reference Citation Analysis]
2 Dell’anna D, Aydemir FB, Dalpiaz F. Evaluating classifiers in SE research: the ECSER pipeline and two replication studies. Empir Software Eng 2023;28:3. [DOI: 10.1007/s10664-022-10243-1] [Reference Citation Analysis]
3 Aagaard N, Larsen AT, Aasvang EK, Meyhoff CS. The impact of continuous wireless monitoring on adverse device effects in medical and surgical wards: a review of current evidence. J Clin Monit Comput 2023;37:7-17. [PMID: 35917046 DOI: 10.1007/s10877-022-00899-x] [Reference Citation Analysis]
4 Verma N, Choudhury A, Singh V, Duseja A, Al-Mahtab M, Devarbhavi H, Eapen CE, Goel A, Ning Q, Duan Z, Hamid S, Jafri W, Butt AS, Shukla A, Tan SS, Kim DJ, Hu J, Sood A, Goel O, Midha V, Ghaznian H, Sahu MK, Lee GH, Treeprasertsuk S, Shah S, Lesmana LA, Lesmana RC, Prasad VGM, Sarin SK; APASL ACLF Research Consortium (AARC) for APASL ACLF Working Party. APASL-ACLF Research Consortium-Artificial Intelligence (AARC-AI) model precisely predicts outcomes in acute-on-chronic liver failure patients. Liver Int 2023;43:442-51. [PMID: 35797245 DOI: 10.1111/liv.15361] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Karabacak M, Margetis K. A Machine Learning-Based Online Prediction Tool for Predicting Short-Term Postoperative Outcomes Following Spinal Tumor Resections. Cancers 2023;15:812. [DOI: 10.3390/cancers15030812] [Reference Citation Analysis]
6 Shrivastava R, Singhal M, Gupta M, Joshi A. Development of an Artificial Intelligence-Guided Citizen-Centric Predictive Model for the Uptake of Maternal Health Services Among Pregnant Women Living in Urban Slum Settings in India: Protocol for a Cross-sectional Study With a Mixed Methods Design. JMIR Res Protoc 2023;12:e35452. [PMID: 36705968 DOI: 10.2196/35452] [Reference Citation Analysis]
7 Labott J, Lu Y, Salmons HI, Camp CL, Wyles CC, Taunton MJ. Health and Socioeconomic Risk Factors for Unplanned Hospitalization Following Ambulatory Unicompartmental Knee Arthroplasty: Development of a Patient Selection Tool Using Machine Learning. J Arthroplasty 2023:S0883-5403(23)00047-5. [PMID: 36709883 DOI: 10.1016/j.arth.2023.01.026] [Reference Citation Analysis]
8 Susanty S, Sufriyana H, Su EC, Chuang YH. Questionnaire-free machine-learning method to predict depressive symptoms among community-dwelling older adults. PLoS One 2023;18:e0280330. [PMID: 36696383 DOI: 10.1371/journal.pone.0280330] [Reference Citation Analysis]
9 Biswas S, Macarthur JI, Pandit A, Mcmenemy L, Sarkar V, Thompson H, Saleemi MS, Chintzewen J, Almansoor ZR, Chai XT, Hardman E, Torrie C, Holt M, Hanna T, Sobieraj A, Toma A, George KJ. Predicting neurosurgical referral outcomes in patients with chronic subdural hematomas using machine learning algorithms – A multi-center feasibility study. Surgical Neurology International 2023;14:22. [DOI: 10.25259/sni_1086_2022] [Reference Citation Analysis]
10 Davoudi A, Sajdeya R, Ison R, Hagen J, Rashidi P, Price CC, Tighe PJ. Fairness in the prediction of acute postoperative pain using machine learning models. Front Digit Health 2022;4:970281. [PMID: 36714611 DOI: 10.3389/fdgth.2022.970281] [Reference Citation Analysis]
11 Cai Y, Chen R, Gao S, Li W, Liu Y, Su G, Song M, Jiang M, Jiang C, Zhang X. Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy. Front Oncol 2022;12:1054231. [PMID: 36698417 DOI: 10.3389/fonc.2022.1054231] [Reference Citation Analysis]
12 Mari T, Asgard O, Henderson J, Hewitt D, Brown C, Stancak A, Fallon N. External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals. Sci Rep 2023;13:242. [PMID: 36604453 DOI: 10.1038/s41598-022-27298-1] [Reference Citation Analysis]
13 Master SR. The Case for Including Data and Code with ML Publications in Laboratory Medicine. J Appl Lab Med 2023;8:213-6. [PMID: 36610411 DOI: 10.1093/jalm/jfac088] [Reference Citation Analysis]
14 Mistry S, Riches NO, Gouripeddi R, Facelli JC. Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review. Artif Intell Med 2023;135:102461. [PMID: 36628796 DOI: 10.1016/j.artmed.2022.102461] [Reference Citation Analysis]
15 Mohamed A, Shuaib A, Ahmed AZ, Saqqur M, Fatima N. Predictors of 30-day mortality using machine learning approach following carotid endarterectomy. Neurol Sci 2023;44:253-61. [PMID: 36104471 DOI: 10.1007/s10072-022-06392-2] [Reference Citation Analysis]
16 Inokuchi R, Iwagami M, Sun Y, Sakamoto A, Tamiya N. Machine learning models predicting undertriage in telephone triage. Annals of Medicine 2022;54:2990-2997. [DOI: 10.1080/07853890.2022.2136402] [Reference Citation Analysis]
17 Sharma V, Kulkarni V, Jess E, Gilani F, Eurich D, Simpson SH, Voaklander D, Semenchuk M, London C, Samanani S. Development and Validation of a Machine Learning Model to Estimate Risk of Adverse Outcomes Within 30 Days of Opioid Dispensation. JAMA Netw Open 2022;5:e2248559. [PMID: 36574245 DOI: 10.1001/jamanetworkopen.2022.48559] [Reference Citation Analysis]
18 Abbas J, Yousef M, Peled N, Hershkovitz I, Hamoud K. Predictive factors for degenerative lumbar spinal stenosis: A model obtained from a machine learning algorithm technique.. [DOI: 10.21203/rs.3.rs-2346084/v1] [Reference Citation Analysis]
19 Zhang JK, Jayasekera D, Javeed S, Greenberg JK, Blum J, Dibble CF, Sun P, Song SK, Ray WZ. Diffusion basis spectrum imaging predicts long-term clinical outcomes following surgery in cervical spondylotic myelopathy. Spine J 2022:S1529-9430(22)01042-7. [PMID: 36509379 DOI: 10.1016/j.spinee.2022.12.003] [Reference Citation Analysis]
20 Kendale S, Bishara A, Burns M, Solomon S, Corriere M, Mathis M. Explainable Machine Learning for Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation (Preprint).. [DOI: 10.2196/preprints.44909] [Reference Citation Analysis]
21 Zhang Y, Li X, Liu Y, Li A, Yang X, Tang X. A method of multi-label text classifier at the publication level for cancer literature (Preprint).. [DOI: 10.2196/preprints.44892] [Reference Citation Analysis]
22 Wong JE, Yamaguchi M, Nishi N, Araki M, Wee LH. Predicting Overweight and Obesity Status Among Malaysian Working Adults With Machine Learning or Logistic Regression: Retrospective Comparison Study. JMIR Form Res 2022;6:e40404. [PMID: 36476813 DOI: 10.2196/40404] [Reference Citation Analysis]
23 van de Kuit A, Oosterhoff JHF, Dijkstra H, Sprague S, Bzovsky S, Bhandari M, Swiontkowski M, Schemitsch EH, IJpma FFA, Poolman RW, Doornberg JN, Hendrickx LAM; the Machine Learning Consortium and FAITH Investigators. Patients With Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine-learning Algorithm. Clin Orthop Relat Res 2022;480:2350-60. [PMID: 35767811 DOI: 10.1097/CORR.0000000000002283] [Reference Citation Analysis]
24 Yoshimura M, Shiramoto H, Koga M, Morimoto Y. Preoperative echocardiography predictive analytics for postinduction hypotension prediction. PLoS One 2022;17:e0278140. [PMID: 36441797 DOI: 10.1371/journal.pone.0278140] [Reference Citation Analysis]
25 Andaur Navarro CL, Damen JAA, van Smeden M, Takada T, Nijman SWJ, Dhiman P, Ma J, Collins GS, Bajpai R, Riley RD, Moons KGM, Hooft L. Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models. J Clin Epidemiol 2022;154:8-22. [PMID: 36436815 DOI: 10.1016/j.jclinepi.2022.11.015] [Reference Citation Analysis]
26 Laios A, De Freitas DLD, Saalmink G, Tan YS, Johnson R, Zubayraeva A, Munot S, Hutson R, Thangavelu A, Broadhead T, Nugent D, Kalampokis E, de Lima KMG, Theophilou G, De Jong D. Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score. Curr Oncol 2022;29:9088-104. [PMID: 36547125 DOI: 10.3390/curroncol29120711] [Reference Citation Analysis]
27 Poveda JL, Bretón-romero R, Del Rio-bermudez C, Taberna M, Medrano IH. How can artificial intelligence optimize value-based contracting? J of Pharm Policy and Pract 2022;15:85. [DOI: 10.1186/s40545-022-00475-3] [Reference Citation Analysis]
28 Prijs J, Liao Z, To M, Verjans J, Jutte PC, Stirler V, Olczak J, Gordon M, Guss D, Digiovanni CW, Jaarsma RL, Ijpma FFA, Doornberg JN, Aksakal K, Barvelink B, Beuker B, Bultra AE, Oliviera LEC, Colaris J, de Klerk H, Duckworth A, ten Duis K, Fennema E, Harbers J, Hendrickx R, Heng M, Hoeksema S, Hogervorst M, Jadav B, Jiang J, Karhade A, Kerkhoffs G, Kuipers J, Laane C, Langerhuizen D, Lubberts B, Mallee W, Mhmud H, El Moumni M, Nieboer P, Nijhuis KO, van Ooijen P, Oosterhoff J, Rawat J, Ring D, Schilstra S, Schwab J, Sprague S, Stufkens S, Tijdens E, van der Bekerom M, van der Vet P, de Vries JP, Wendt K, Wijffels M, Worsley D, the Machine Learning Consortium. Development and external validation of automated detection, classification, and localization of ankle fractures: inside the black box of a convolutional neural network (CNN). Eur J Trauma Emerg Surg 2022. [DOI: 10.1007/s00068-022-02136-1] [Reference Citation Analysis]
29 Goedmakers CMW, Pereboom LM, Schoones JW, de Leeuw den Bouter ML, Remis RF, Staring M, Vleggeert-Lankamp CLA. Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods. Brain Spine 2022;2:101666. [PMID: 36506292 DOI: 10.1016/j.bas.2022.101666] [Reference Citation Analysis]
30 Ferguson LB, Mayfield RD, Messing RO. RNA biomarkers for alcohol use disorder. Front Mol Neurosci 2022;15:1032362. [PMID: 36407766 DOI: 10.3389/fnmol.2022.1032362] [Reference Citation Analysis]
31 Lu Y, Jurgensmeier K, Till SE, Reinholz A, Saris DBF, Camp CL, Krych AJ. Early ACLR and Risk and Timing of Secondary Meniscal Injury Compared With Delayed ACLR or Nonoperative Treatment: A Time-to-Event Analysis Using Machine Learning. Am J Sports Med 2022;50:3544-56. [PMID: 36178166 DOI: 10.1177/03635465221124258] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Kunze KN, Kaidi A, Madjarova S, Polce EM, Ranawat AS, Nawabi DH, Kelly BT, Nho SJ, Nwachukwu BU. External Validation of a Machine Learning Algorithm for Predicting Clinically Meaningful Functional Improvement After Arthroscopic Hip Preservation Surgery. Am J Sports Med 2022;50:3593-9. [PMID: 36135373 DOI: 10.1177/03635465221124275] [Reference Citation Analysis]
33 Lithy RM, Omar Abdelaziz A, Awad A, Ibrahim Shousha H, Omran D, Mahmoud Nabil M, Hosni Abdelmaksoud A, Mahmoud Elbaz T, Mabrouk M. Meta-learning algorithm development to predict outcomes in patients with hepatitis C virus-related hepatocellular carcinoma. Arab J Gastroenterol 2022;23:230-4. [PMID: 36400702 DOI: 10.1016/j.ajg.2022.10.008] [Reference Citation Analysis]
34 Post B, Badea C, Faisal A, Brett SJ. Breaking bad news in the era of artificial intelligence and algorithmic medicine: an exploration of disclosure and its ethical justification using the hedonic calculus. AI Ethics 2022. [DOI: 10.1007/s43681-022-00230-z] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
35 Castela Forte J, Yeshmagambetova G, van der Grinten ML, Scheeren TWL, Nijsten MWN, Mariani MA, Henning RH, Epema AH. Comparison of Machine Learning Models Including Preoperative, Intraoperative, and Postoperative Data and Mortality After Cardiac Surgery. JAMA Netw Open 2022;5:e2237970. [DOI: 10.1001/jamanetworkopen.2022.37970] [Reference Citation Analysis]
36 Zhang W, Zheng X, Li R, Liu M, Xiao W, Huang L, Xu F, Dong N, Li Y. Research on nonstroke dementia screening and cognitive function prediction model for older people based on brain atrophy characteristics. Brain and Behavior 2022. [DOI: 10.1002/brb3.2726] [Reference Citation Analysis]
37 Salmons HI, Lu Y, Labott JR, Wyles CC, Camp CL, Taunton MJ. Identifying Modifiable Cost Drivers of Outpatient Unicompartmental Knee Arthroplasty With Machine Learning. J Arthroplasty 2022:S0883-5403(22)00937-8. [PMID: 36265720 DOI: 10.1016/j.arth.2022.10.017] [Reference Citation Analysis]
38 Fehr J, Jaramillo-Gutierrez G, Oala L, Gröschel MI, Bierwirth M, Balachandran P, Werneck-Leite A, Lippert C. Piloting a Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI Tools. Healthcare (Basel) 2022;10:1923. [PMID: 36292369 DOI: 10.3390/healthcare10101923] [Reference Citation Analysis]
39 Li J, Kaifa Z. Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross-Border Data Flow Governance. Journal of Environmental and Public Health 2022;2022:1-12. [DOI: 10.1155/2022/6105804] [Reference Citation Analysis]
40 Wei X, Yan XJ, Guo YY, Zhang J, Wang GR, Fayyaz A, Yu J. Machine learning-based gray-level co-occurrence matrix signature for predicting lymph node metastasis in undifferentiated-type early gastric cancer. World J Gastroenterol 2022; 28(36): 5338-5350 [DOI: 10.3748/wjg.v28.i36.5338] [Reference Citation Analysis]
41 Cunha MT, de Souza Borges AP, Carvalho Jardim V, Fujita A, de Castro G Jr. Predicting survival in metastatic non-small cell lung cancer patients with poor ECOG-PS: A single-arm prospective study. Cancer Med 2022. [PMID: 36161783 DOI: 10.1002/cam4.5254] [Reference Citation Analysis]
42 Banda JM, Shah NH, Periyakoil VS. Characterizing subgroup performance of probabilistic phenotype algorithms within older adults: A case study for dementia, mild cognitive impairment, and Alzheimer’s and Parkinson’s diseases.. [DOI: 10.1101/2022.09.20.22280172] [Reference Citation Analysis]
43 Azeli Y, Fernández A, Capriles F, Rojewski W, Lopez-madrid V, Sabaté-lissner D, Serrano RM, Rey-reñones C, Civit M, Casellas J, El Ouahabi-el Ouahabi A, Foglia-fernández M, Sarrá S, Llobet E. A machine learning COVID-19 mass screening based on symptoms and a simple olfactory test. Sci Rep 2022;12. [DOI: 10.1038/s41598-022-19817-x] [Reference Citation Analysis]
44 Hossain MZ, Daskalaki E, Brüstle A, Desborough J, Lueck CJ, Suominen H. The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review. BMC Med Inform Decis Mak 2022;22. [DOI: 10.1186/s12911-022-01985-5] [Reference Citation Analysis]
45 Lu J, Sattler A, Wang S, Khaki AR, Callahan A, Fleming S, Fong R, Ehlert B, Li RC, Shieh L, Ramchandran K, Gensheimer MF, Chobot S, Pfohl S, Li S, Shum K, Parikh N, Desai P, Seevaratnam B, Hanson M, Smith M, Xu Y, Gokhale A, Lin S, Pfeffer MA, Teuteberg W, Shah NH. Considerations in the reliability and fairness audits of predictive models for advance care planning. Front Digit Health 2022;4. [DOI: 10.3389/fdgth.2022.943768] [Reference Citation Analysis]
46 de Graaf WM, van Riet TCT, de Lange J, Kober J. A Multiclass Classification Model for Tooth Removal Procedures. J Dent Res 2022;:220345221117745. [PMID: 36085583 DOI: 10.1177/00220345221117745] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
47 Salmons HI, Lu Y, Reed RR, Forsythe B, Sebastian AS. Implementation of Machine Learning to Predict Cost of Care Associated with Ambulatory Single Level Lumbar Decompression. World Neurosurg 2022:S1878-8750(22)01276-1. [PMID: 36089278 DOI: 10.1016/j.wneu.2022.08.149] [Reference Citation Analysis]
48 Wu T, Wei Y, Wu J, Yi B, Li H. Logistic regression technique is comparable to machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome.. [DOI: 10.21203/rs.3.rs-2018412/v1] [Reference Citation Analysis]
49 Błaziak M, Urban S, Wietrzyk W, Jura M, Iwanek G, Stańczykiewicz B, Kuliczkowski W, Zymliński R, Pondel M, Berka P, Danel D, Biegus J, Siennicka A. An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review. Biomedicines 2022;10:2188. [DOI: 10.3390/biomedicines10092188] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
50 Davis SE, Walsh CG, Matheny ME. Open questions and research gaps for monitoring and updating AI-enabled tools in clinical settings. Front Digit Health 2022;4. [DOI: 10.3389/fdgth.2022.958284] [Reference Citation Analysis]
51 Wang F, Fan L, Zhao Q, Liu Y, Zhang Z, Wang D, Zhao X, Li Y, Tan B. Family history of malignant tumor is a predictor of gastric cancer prognosis: Incorporation into a nomogram. Medicine 2022;101:e30141. [DOI: 10.1097/md.0000000000030141] [Reference Citation Analysis]
52 Bowers A, Drake C, Makarkin AE, Monzyk R, Maity B, Telle A. Features of a Machine Learning Model Predicting Mortality for Earlier Palliative Care Identification in Medicare Advantage Plans (Preprint).. [DOI: 10.2196/preprints.42253] [Reference Citation Analysis]
53 Russo V, Lallo E, Munnia A, Spedicato M, Messerini L, D'Aurizio R, Ceroni EG, Brunelli G, Galvano A, Russo A, Landini I, Nobili S, Ceppi M, Bruzzone M, Cianchi F, Staderini F, Roselli M, Riondino S, Ferroni P, Guadagni F, Mini E, Peluso M. Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022;14:4012. [PMID: 36011003 DOI: 10.3390/cancers14164012] [Reference Citation Analysis]
54 Jurgensmeier K, Till SE, Lu Y, Arguello AM, Stuart MJ, Saris DBF, Camp CL, Krych AJ. Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention. Knee Surg Sports Traumatol Arthrosc 2022. [DOI: 10.1007/s00167-022-07117-w] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
55 Bulstra AEJ; Machine Learning Consortium. A Machine Learning Algorithm to Estimate the Probability of a True Scaphoid Fracture After Wrist Trauma. J Hand Surg Am 2022;47:709-18. [PMID: 35667955 DOI: 10.1016/j.jhsa.2022.02.023] [Reference Citation Analysis]
56 Rudisill SS, Hornung AL, Barajas JN, Bridge JJ, Mallow GM, Lopez W, Sayari AJ, Louie PK, Harada GK, Tao Y, Wilke HJ, Colman MW, Phillips FM, An HS, Samartzis D. Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion. Eur Spine J 2022;31:2104-14. [PMID: 35543762 DOI: 10.1007/s00586-022-07238-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
57 Lu JH, Callahan A, Patel BS, Morse KE, Dash D, Pfeffer MA, Shah NH. Assessment of Adherence to Reporting Guidelines by Commonly Used Clinical Prediction Models From a Single Vendor: A Systematic Review. JAMA Netw Open 2022;5:e2227779. [PMID: 35984654 DOI: 10.1001/jamanetworkopen.2022.27779] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
58 O'Connor S, Yan Y, Thilo FJS, Felzmann H, Dowding D, Lee JJ. Artificial intelligence in nursing and midwifery: A systematic review. J Clin Nurs 2022. [PMID: 35908207 DOI: 10.1111/jocn.16478] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
59 Sedlakova J, Daniore P, Wintsch AH, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Ettlin DA, Grübner O, Rinaldi F, von Wyl V, University of Zurich Digital Society Initiative (UZH-DSI) Health Community. Challenges and best practices for digital unstructured data enrichment in health research: a systematic narrative review.. [DOI: 10.1101/2022.07.28.22278137] [Reference Citation Analysis]
60 Suh J, Lee SW. Preoperative prediction of the need for arterial and central venous catheterization using machine learning techniques. Sci Rep 2022;12:11948. [PMID: 35831346 DOI: 10.1038/s41598-022-16144-z] [Reference Citation Analysis]
61 Lee SW, Lee HC, Suh J, Lee KH, Lee H, Seo S, Kim TK, Lee SW, Kim YJ. Multi-center validation of machine learning model for preoperative prediction of postoperative mortality. NPJ Digit Med 2022;5:91. [PMID: 35821515 DOI: 10.1038/s41746-022-00625-6] [Reference Citation Analysis]
62 Lu J, Sattler A, Wang S, Khaki AR, Callahan A, Fleming S, Fong R, Ehlert B, Li RC, Shieh L, Ramchandran K, Gensheimer MF, Chobot S, Pfohl S, Li S, Shum K, Parikh N, Desai P, Seevaratnam B, Hanson M, Smith M, Xu Y, Gokhale A, Lin S, Pfeffer MA, Teuteberg W, Shah NH. Considerations in the Reliability and Fairness Audits of Predictive Models for Advance Care Planning.. [DOI: 10.1101/2022.07.10.22275967] [Reference Citation Analysis]
63 Kothari R, Chiu C, Moukheiber M, Jehiro M, Bishara A, Lee C, Piracchio R, Celi LA. A descriptive appraisal of quality of reporting in a cohort of machine learning studies in anesthesiology. Anaesth Crit Care Pain Med 2022;41:101126. [PMID: 35811037 DOI: 10.1016/j.accpm.2022.101126] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
64 Bai Z, Zhang J, Tang C, Wang L, Xia W, Qi Q, Lu J, Fang Y, Fong KNK, Niu W. Return-to-Work Predictions for Chinese Patients With Occupational Upper Extremity Injury: A Prospective Cohort Study. Front Med 2022;9:805230. [DOI: 10.3389/fmed.2022.805230] [Reference Citation Analysis]
65 Liao WW, Hsieh YW, Lee TH, Chen CL, Wu CY. Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke. Sci Rep 2022;12:11235. [PMID: 35787657 DOI: 10.1038/s41598-022-14986-1] [Reference Citation Analysis]
66 Lu Y, Pareek A, Lavoie-gagne OZ, Forlenza EM, Patel BH, Reinholz AK, Forsythe B, Camp CL. Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes. Orthopaedic Journal of Sports Medicine 2022;10:232596712211117. [DOI: 10.1177/23259671221111742] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
67 Lu Y, Lavoie-Gagne O, Forlenza EM, Pareek A, Kunze KN, Forsythe B, Levy BA, Krych AJ. Duration of Care and Operative Time Are the Primary Drivers of Total Charges After Ambulatory Hip Arthroscopy: A Machine Learning Analysis. Arthroscopy 2022;38:2204-2216.e3. [PMID: 34921955 DOI: 10.1016/j.arthro.2021.12.012] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
68 Yen HK, Ogink PT, Huang CC, Groot OQ, Su CC, Chen SF, Chen CW, Karhade AV, Peng KP, Lin WH, Chiang H, Yang JJ, Dai SH, Yen MH, Verlaan JJ, Schwab JH, Wong TH, Yang SH, Hu MH. A machine learning algorithm for predicting prolonged postoperative opioid prescription after lumbar disc herniation surgery. An external validation study using 1,316 patients from a Taiwanese cohort. Spine J 2022;22:1119-30. [PMID: 35202784 DOI: 10.1016/j.spinee.2022.02.009] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
69 van Boven MR, Henke CE, Leemhuis AG, Hoogendoorn M, van Kaam AH, Königs M, Oosterlaan J. Machine Learning Prediction Models for Neurodevelopmental Outcome After Preterm Birth: A Scoping Review and New Machine Learning Evaluation Framework. Pediatrics 2022;150. [DOI: 10.1542/peds.2021-056052] [Reference Citation Analysis]
70 Martin VP, Rouas JL, Philip P, Fourneret P, Micoulaud-Franchi JA, Gauld C. How Does Comparison With Artificial Intelligence Shed Light on the Way Clinicians Reason? A Cross-Talk Perspective. Front Psychiatry 2022;13:926286. [PMID: 35757203 DOI: 10.3389/fpsyt.2022.926286] [Reference Citation Analysis]
71 Dwivedi AK. How to write statistical analysis section in medical research. J Investig Med 2022:jim-2022-002479. [PMID: 35710142 DOI: 10.1136/jim-2022-002479] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
72 Stafford IS, Gosink MM, Mossotto E, Ennis S, Hauben M. A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation. Inflamm Bowel Dis 2022;28:1573-83. [PMID: 35699597 DOI: 10.1093/ibd/izac115] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
73 Elezaby MA. Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice: A Methodologic Framework for Clinical Testing of Artificial Intelligence Tools. J Am Coll Radiol 2022:S1546-1440(22)00357-X. [PMID: 35690078 DOI: 10.1016/j.jacr.2022.05.013] [Reference Citation Analysis]
74 Yossofzai O, Fallah A, Maniquis C, Wang S, Ragheb J, Weil AG, Brunette-Clement T, Andrade A, Ibrahim GM, Mitsakakis N, Widjaja E. Development and validation of machine learning models for prediction of seizure outcome after pediatric epilepsy surgery. Epilepsia 2022. [PMID: 35661152 DOI: 10.1111/epi.17320] [Reference Citation Analysis]
75 Meng J, Liu Z, Xu X. Applications of neural networks in liver transplantation. iLIVER 2022;1:101-110. [DOI: 10.1016/j.iliver.2022.07.002] [Reference Citation Analysis]
76 Pareek A, Martin RK. Editorial Commentary: Machine Learning in Medicine Requires Clinician Input, Faces Barriers, and High-Quality Evidence Is Required to Demonstrate Improved Patient Outcomes. Arthroscopy 2022;38:2106-8. [PMID: 35660191 DOI: 10.1016/j.arthro.2022.01.026] [Reference Citation Analysis]
77 Ranasinghe JC, Jain A, Wu W, Zhang K, Wang Z, Huang S. Engineered 2D materials for optical bioimaging and path toward therapy and tissue engineering. Journal of Materials Research. [DOI: 10.1557/s43578-022-00591-5] [Reference Citation Analysis]
78 Fardouly J, Crosby RD, Sukunesan S. Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions. J Eat Disord 2022;10. [DOI: 10.1186/s40337-022-00581-2] [Reference Citation Analysis]
79 Polce EM, Kunze KN, Dooley MS, Piuzzi NS, Boettner F, Sculco PK. Efficacy and Applications of Artificial Intelligence and Machine Learning Analyses in Total Joint Arthroplasty: A Call for Improved Reporting. J Bone Joint Surg Am 2022;104:821-32. [PMID: 35045061 DOI: 10.2106/JBJS.21.00717] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
80 Cresta Morgado P, Carusso M, Alonso Alemany L, Acion L. Practical foundations of machine learning for addiction research. Part II. Workflow and use cases. The American Journal of Drug and Alcohol Abuse 2022;48:272-283. [DOI: 10.1080/00952990.2021.1966435] [Reference Citation Analysis]
81 Kunze KN, Karhade AV, Polce EM, Schwab JH, Levine BR. Development and internal validation of machine learning algorithms for predicting complications after primary total hip arthroplasty. Arch Orthop Trauma Surg 2022. [PMID: 35508549 DOI: 10.1007/s00402-022-04452-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
82 Morgenstern JD, Rosella LC, Costa AP, Anderson LN. Development of machine learning prediction models to explore nutrients predictive of cardiovascular disease using Canadian linked population-based data. Appl Physiol Nutr Metab 2022;47:529-46. [PMID: 35113677 DOI: 10.1139/apnm-2021-0502] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
83 Anderson AB, Grazal C, Wedin R, Kuo C, Chen Y, Christensen BR, Cullen J, Forsberg JA. Machine learning algorithms to estimate 10-Year survival in patients with bone metastases due to prostate cancer: toward a disease-specific survival estimation tool. BMC Cancer 2022;22:476. [PMID: 35490227 DOI: 10.1186/s12885-022-09491-7] [Reference Citation Analysis]
84 Harrison CJ, Geoghegan L, Sidey-gibbons CJ, Stirling PHC, Mceachan JE, Rodrigues JN. Developing Machine Learning Algorithms to Support Patient-centered, Value-based Carpal Tunnel Decompression Surgery. Plastic and Reconstructive Surgery - Global Open 2022;10:e4279. [DOI: 10.1097/gox.0000000000004279] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
85 Marotta L, Scheltinga BL, van Middelaar R, Bramer WM, van Beijnum BF, Reenalda J, Buurke JH. Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review. Sensors (Basel) 2022;22:3008. [PMID: 35458993 DOI: 10.3390/s22083008] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
86 Al-Zaiti SS, Alghwiri AA, Hu X, Clermont G, Peace A, Macfarlane P, Bond R. A clinician's guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML). Eur Heart J Digit Health 2022;3:125-40. [PMID: 36713011 DOI: 10.1093/ehjdh/ztac016] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
87 Pantelis AG, Panagopoulou PA, Lapatsanis DP. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review. Diagnostics 2022;12:874. [DOI: 10.3390/diagnostics12040874] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
88 Oosterhoff JHF, Gravesteijn BY, Karhade AV, Jaarsma RL, Kerkhoffs GMMJ, Ring D, Schwab JH, Steyerberg EW, Doornberg JN; Machine Learning Consortium. Feasibility of Machine Learning and Logistic Regression Algorithms to Predict Outcome in Orthopaedic Trauma Surgery. J Bone Joint Surg Am 2022;104:544-51. [PMID: 34921550 DOI: 10.2106/JBJS.21.00341] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
89 Ngombu S, Binol H, Gurcan MN, Moberly AC. Advances in Artificial Intelligence to Diagnose Otitis Media: State of the Art Review. Otolaryngol Head Neck Surg 2022;:1945998221083502. [PMID: 35290142 DOI: 10.1177/01945998221083502] [Reference Citation Analysis]
90 Ramlakhan S, Saatchi R, Sabir L, Singh Y, Hughes R, Shobayo O, Ventour D. Understanding and interpreting artificial intelligence, machine learning and deep learning in Emergency Medicine. Emerg Med J 2022:emermed-2021-212068. [PMID: 35241440 DOI: 10.1136/emermed-2021-212068] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
91 Ramlakhan SL, Saatchi R, Sabir L, Ventour D, Shobayo O, Hughes R, Singh Y. Building artificial intelligence and machine learning models : a primer for emergency physicians. Emerg Med J 2022:emermed-2022-212379. [PMID: 35241439 DOI: 10.1136/emermed-2022-212379] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
92 Weaver CGW, Basmadjian RB, Williamson T, McBrien K, Sajobi T, Boyne D, Yusuf M, Ronksley PE. Reporting of Model Performance and Statistical Methods in Studies That Use Machine Learning to Develop Clinical Prediction Models: Protocol for a Systematic Review. JMIR Res Protoc 2022;11:e30956. [PMID: 35238322 DOI: 10.2196/30956] [Reference Citation Analysis]
93 Kunze KN, Polce EM, Clapp IM, Alter T, Nho SJ. Association Between Preoperative Patient Factors and Clinically Meaningful Outcomes After Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Machine Learning Analysis. Am J Sports Med 2022;50:746-56. [PMID: 35006010 DOI: 10.1177/03635465211067546] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
94 Cullen MR, Baiocchi M, Chamberlain L, Chu I, Horwitz RI, Mello M, O'hara A, Roosz S. Population health science as a unifying foundation for translational clinical and public health research. SSM - Population Health 2022. [DOI: 10.1016/j.ssmph.2022.101047] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
95 Grazal CF, Anderson AB, Booth GJ, Geiger PG, Forsberg JA, Balazs GC. A Machine-Learning Algorithm to Predict the Likelihood of Prolonged Opioid Use Following Arthroscopic Hip Surgery. Arthroscopy 2022;38:839-847.e2. [PMID: 34411683 DOI: 10.1016/j.arthro.2021.08.009] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
96 Kirtley OJ, van Mens K, Hoogendoorn M, Kapur N, de Beurs D. Translating promise into practice: a review of machine learning in suicide research and prevention. The Lancet Psychiatry 2022;9:243-52. [DOI: 10.1016/s2215-0366(21)00254-6] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
97 Jones DT, Kerber KA. Artificial Intelligence and the Practice of Neurology in 2035: The Neurology Future Forecasting Series. Neurology 2022;98:238-45. [PMID: 35131918 DOI: 10.1212/WNL.0000000000013200] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
98 Kunze KN, Sculco PK, Zhong H, Memtsoudis SG, Ast MP, Sculco TP, Jules-Elysee KM. Development and Internal Validation of Machine Learning Algorithms for Predicting Hyponatremia After TJA. J Bone Joint Surg Am 2022;104:265-70. [PMID: 34898530 DOI: 10.2106/JBJS.21.00718] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
99 Hu Q, Chen K, Liu F, Zhao M, Liang F, Xue D. Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte. Materials (Basel) 2022;15:1157. [PMID: 35161101 DOI: 10.3390/ma15031157] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
100 Chen AB, Haque T, Roberts S, Rambhatla S, Cacciamani G, Dasgupta P, Hung AJ. Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists. Urol Clin North Am 2022;49:65-117. [PMID: 34776055 DOI: 10.1016/j.ucl.2021.07.009] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
101 Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, Klang E. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Acad Radiol 2022;29 Suppl 2:S226-35. [PMID: 34219012 DOI: 10.1016/j.acra.2021.05.014] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
102 Mládek A, Gerla V, Skalický P, Vlasák A, Zazay A, Lhotská L, Beneš V Sr, Beneš V Jr, Bradáč O. Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach. Neurosurgery 2022. [PMID: 35080523 DOI: 10.1227/NEU.0000000000001838] [Reference Citation Analysis]
103 Mhs N, Elsaid M, Paskett J, Bose-brill S, Bridges JFP. Oversight of artificial intelligence in medicine: A review of frameworks (Preprint).. [DOI: 10.2196/preprints.36823] [Reference Citation Analysis]
104 Mhs N, Elsaid M, Paskett J, Bose-brill S, Bridges JFP. Guidelines for artificial intelligence in medicine: A literature review and content analysis of frameworks (Preprint). Journal of Medical Internet Research. [DOI: 10.2196/36823] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
105 de Hond AAH, Leeuwenberg AM, Hooft L, Kant IMJ, Nijman SWJ, van Os HJA, Aardoom JJ, Debray TPA, Schuit E, van Smeden M, Reitsma JB, Steyerberg EW, Chavannes NH, Moons KGM. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. NPJ Digit Med 2022;5:2. [PMID: 35013569 DOI: 10.1038/s41746-021-00549-7] [Cited by in Crossref: 18] [Cited by in F6Publishing: 21] [Article Influence: 18.0] [Reference Citation Analysis]
106 Haymond S, Master SR. How Can We Ensure Reproducibility and Clinical Translation of Machine Learning Applications in Laboratory Medicine? Clin Chem 2022;68:392-5. [PMID: 35019992 DOI: 10.1093/clinchem/hvab272] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
107 Song BM, Lu Y, Wilbur RR, Lavoie-Gagne O, Pareek A, Forsythe B, Krych AJ. Machine Learning Model Identifies Increased Operative Time and Greater BMI as Predictors for Overnight Admission After Outpatient Hip Arthroscopy. Arthrosc Sports Med Rehabil 2021;3:e1981-90. [PMID: 34977657 DOI: 10.1016/j.asmr.2021.10.001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
108 Yee TS, Shrifan NHMM, Al-gburi AJA, Isa NAM, Akbar MF. Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review. IEEE Access 2022;10:88191-210. [DOI: 10.1109/access.2022.3197291] [Reference Citation Analysis]
109 Chen Y, Xi M, Johnson A, Tomlinson G, Campigotto A, Chen L, Sung L. Machine Learning Approaches to Investigate Clostridioides difficile Infection and Outcomes: A Systematic Review. International Journal of Medical Informatics 2022. [DOI: 10.1016/j.ijmedinf.2022.104706] [Reference Citation Analysis]
110 Lopez-ramos LM. Future Perspectives on Automated Machine Learning in Biomedical Signal Processing. Communications in Computer and Information Science 2022. [DOI: 10.1007/978-3-031-10525-8_13] [Reference Citation Analysis]
111 da Silva Neto SR, Tabosa Oliveira T, Teixeira IV, Aguiar de Oliveira SB, Souza Sampaio V, Lynn T, Endo PT. Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review. PLoS Negl Trop Dis 2022;16:e0010061. [PMID: 35025860 DOI: 10.1371/journal.pntd.0010061] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
112 Ng WT, But B, Choi HC, de Bree R, Lee AW, Lee VH, López F, Mäkitie AA, Rodrigo JP, Saba NF, Tsang RK, Ferlito A. Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review. CMAR 2022;Volume 14:339-66. [DOI: 10.2147/cmar.s341583] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
113 Kunze KN, Orr M, Krebs V, Bhandari M, Piuzzi NS. Potential benefits, unintended consequences, and future roles of artificial intelligence in orthopaedic surgery research : a call to emphasize data quality and indications. Bone Jt Open 2022;3:93-7. [PMID: 35084227 DOI: 10.1302/2633-1462.31.BJO-2021-0123.R1] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
114 Magal N, Rab SL, Goldstein P, Simon L, Jiryis T, Admon R. Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors. Chronic Stress (Thousand Oaks) 2022;6:24705470221100987. [PMID: 35911618 DOI: 10.1177/24705470221100987] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
115 De Gheselle S, Jacques C, Chambost J, Blank C, Declerck K, De Croo I, Hickman C, Tilleman K. Machine learning for prediction of euploidy in human embryos: in search of the best-performing model and predictive features. Fertility and Sterility 2022. [DOI: 10.1016/j.fertnstert.2021.11.029] [Reference Citation Analysis]
116 Dee EC, Yu RC, Celi LA, Nehal US. AIM and Business Models of Healthcare. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-64573-1_247] [Reference Citation Analysis]
117 Yezhova O, Pashkevich K, Kolosnichenko O, Gerasymenko O, Kolosnichenko M. Forecasted labor functions of fashion industry specialists. INTERNATIONAL CONFERENCE ON TEXTILE AND APPAREL INNOVATION (ICTAI 2021) 2022. [DOI: 10.1063/5.0076957] [Reference Citation Analysis]
118 Schwarzerova J, Kostoval A, Bajger A, Jakubikova L, Pierides I, Popelinsky L, Sedlar K, Weckwerth W. A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling. Advances in Intelligent Systems and Computing 2022. [DOI: 10.1007/978-3-031-09135-3_42] [Reference Citation Analysis]
119 Kalpana, Srivastava A, Jha S. Data-driven machine learning: A new approach to process and utilize biomedical data. Predictive Modeling in Biomedical Data Mining and Analysis 2022. [DOI: 10.1016/b978-0-323-99864-2.00017-2] [Reference Citation Analysis]
120 Ben Ali W, Pesaranghader A, Avram R, Overtchouk P, Perrin N, Laffite S, Cartier R, Ibrahim R, Modine T, Hussin JG. Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble. Front Cardiovasc Med 2021;8:711401. [PMID: 34957230 DOI: 10.3389/fcvm.2021.711401] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
121 Laios A, De Oliveira Silva RV, Dantas De Freitas DL, Tan YS, Saalmink G, Zubayraeva A, Johnson R, Kaufmann A, Otify M, Hutson R, Thangavelu A, Broadhead T, Nugent D, Theophilou G, Gomes de Lima KM, De Jong D. Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score. J Clin Med 2021;11:87. [PMID: 35011828 DOI: 10.3390/jcm11010087] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
122 Alhasan AS. Clinical Applications of Artificial Intelligence, Machine Learning, and Deep Learning in the Imaging of Gliomas: A Systematic Review. Cureus 2021;13:e19580. [PMID: 34926051 DOI: 10.7759/cureus.19580] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
123 Sharma BPharm V, Kulkarni V, McAlister F, Eurich D, Keshwani S, Simpson SH, Voaklander D, Samanani S. Predicting 30-day readmissions in patients with heart failure using administrative data: a machine learning approach. J Card Fail 2021:S1071-9164(21)00499-1. [PMID: 34936894 DOI: 10.1016/j.cardfail.2021.12.004] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
124 Maki S, Furuya T, Yoshii T, Egawa S, Sakai K, Kusano K, Nakagawa Y, Hirai T, Wada K, Katsumi K, Fujii K, Kimura A, Nagoshi N, Kanchiku T, Nagamoto Y, Oshima Y, Ando K, Takahata M, Mori K, Nakajima H, Murata K, Matsunaga S, Kaito T, Yamada K, Kobayashi S, Kato S, Ohba T, Inami S, Fujibayashi S, Katoh H, Kanno H, Imagama S, Koda M, Kawaguchi Y, Takeshita K, Matsumoto M, Ohtori S, Yamazaki M, Okawa A. Machine Learning Approach in Predicting Clinically Significant Improvements After Surgery in Patients with Cervical Ossification of the Posterior Longitudinal Ligament. Spine (Phila Pa 1976) 2021;46:1683-9. [PMID: 34027925 DOI: 10.1097/BRS.0000000000004125] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
125 Zupancic Cepic L, Frank M, Reisinger AG, Sagl B, Pahr DH, Zechner W, Schedle A. Experimental validation of a micro-CT finite element model of a human cadaveric mandible rehabilitated with short-implant-supported partial dentures. J Mech Behav Biomed Mater 2022;126:105033. [PMID: 34933158 DOI: 10.1016/j.jmbbm.2021.105033] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
126 Akingboye A, Mahmood F, Amiruddin N, Reay M, Nightingale P, Ogunwobi OO. Increased risk of COVID-19-related admissions in patients with active solid organ cancer in the West Midlands region of the UK: a retrospective cohort study. BMJ Open 2021;11:e053352. [PMID: 34903546 DOI: 10.1136/bmjopen-2021-053352] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
127 Lu Y, Pareek A, Wilbur RR, Leland DP, Krych AJ, Camp CL. Understanding Anterior Shoulder Instability Through Machine Learning: New Models That Predict Recurrence, Progression to Surgery, and Development of Arthritis. Orthop J Sports Med 2021;9:23259671211053326. [PMID: 34888391 DOI: 10.1177/23259671211053326] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
128 Volpe S, Pepa M, Zaffaroni M, Bellerba F, Santamaria R, Marvaso G, Isaksson LJ, Gandini S, Starzyńska A, Leonardi MC, Orecchia R, Alterio D, Jereczek-Fossa BA. Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist. Front Oncol 2021;11:772663. [PMID: 34869010 DOI: 10.3389/fonc.2021.772663] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
129 De Silva K, Enticott J, Barton C, Forbes A, Saha S, Nikam R. Use and performance of machine learning models for type 2 diabetes prediction in clinical and community care settings: Protocol for a systematic review and meta-analysis of predictive modeling studies. Digit Health 2021;7:20552076211047390. [PMID: 34868616 DOI: 10.1177/20552076211047390] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
130 Lu Y, Kunze K, Cohn MR, Lavoie-gagne O, Polce E, Nwachukwu BU, Forsythe B. Artificial Intelligence Predicts Cost After Ambulatory Anterior Cruciate Ligament Reconstruction. Arthroscopy, Sports Medicine, and Rehabilitation 2021;3:e2033-e2045. [DOI: 10.1016/j.asmr.2021.10.013] [Reference Citation Analysis]
131 De la Garza Salazar F, Romero Ibarguengoitia ME, Azpiri López JR, González Cantú A. Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning. PLoS One 2021;16:e0260661. [PMID: 34847202 DOI: 10.1371/journal.pone.0260661] [Reference Citation Analysis]
132 Bellocchio F, Lonati C, Ion Titapiccolo J, Nadal J, Meiselbach H, Schmid M, Baerthlein B, Tschulena U, Schneider M, Schultheiss UT, Barbieri C, Moore C, Steppan S, Eckardt KU, Stuard S, Neri L. Validation of a Novel Predictive Algorithm for Kidney Failure in Patients Suffering from Chronic Kidney Disease: The Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD). Int J Environ Res Public Health 2021;18:12649. [PMID: 34886378 DOI: 10.3390/ijerph182312649] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
133 Barboi C, Tzavelis A, Muhammad LN. Comparison of Severity of Illness Scores and Artificial Intelligence Models That Are Predictive of Intensive Care Unit Mortality: Meta-analysis and Review of the Literature (Preprint).. [DOI: 10.2196/preprints.35293] [Reference Citation Analysis]
134 Barboi C, Tzavelis A, Muhammad LN. Comparison of Severity of Illness Scores and Artificial Intelligence Models Predictive of Intensive Care Unit Mortality: Meta-analysis and review of the literature (Preprint). JMIR Medical Informatics. [DOI: 10.2196/35293] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
135 Sardina DS, Valenti G, Papia F, Uasuf CG. Exploring Machine Learning Techniques to Predict the Response to Omalizumab in Chronic Spontaneous Urticaria. Diagnostics (Basel) 2021;11:2150. [PMID: 34829497 DOI: 10.3390/diagnostics11112150] [Reference Citation Analysis]
136 Nijman S, Leeuwenberg AM, Beekers I, Verkouter I, Jacobs J, Bots ML, Asselbergs FW, Moons K, Debray T. Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review. J Clin Epidemiol 2021;142:218-29. [PMID: 34798287 DOI: 10.1016/j.jclinepi.2021.11.023] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
137 Kundu A, Chaiton M, Billington R, Grace D, Fu R, Logie C, Baskerville B, Yager C, Mitsakakis N, Schwartz R. Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review. JMIR Med Inform 2021;9:e28962. [PMID: 34762059 DOI: 10.2196/28962] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
138 Shi H, Yang D, Tang K, Hu C, Li L, Zhang L, Gong T, Cui Y. Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease. Clin Nutr 2021;41:202-10. [PMID: 34906845 DOI: 10.1016/j.clnu.2021.11.006] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
139 Seghier ML. Ten simple rules for reporting machine learning methods implementation and evaluation on biomedical data. Int J Imaging Syst Tech 2022;32:5-11. [DOI: 10.1002/ima.22674] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
140 Nerella S, Cupka J, Ruppert M, Tighe P, Bihorac A, Rashidi P. Pain Action Unit Detection in Critically Ill Patients. Proc COMPSAC 2021;2021:645-51. [PMID: 34723289 DOI: 10.1109/compsac51774.2021.00094] [Reference Citation Analysis]
141 Rocha TAH, de Thomaz EBAF, de Almeida DG, da Silva NC, Queiroz RCDS, Andrade L, Facchini LA, Sartori MLL, Costa DB, Campos MAG, da Silva AAM, Staton C, Vissoci JRN. Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach. The Lancet Regional Health - Americas 2021;3:100053. [DOI: 10.1016/j.lana.2021.100053] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
142 Saputro SA, Pattanaprateep O, Pattanateepapon A, Karmacharya S, Thakkinstian A. Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis. Syst Rev 2021;10:288. [PMID: 34724973 DOI: 10.1186/s13643-021-01841-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
143 Kunze KN, Polce EM, Ranawat AS, Randsborg PH, Williams RJ 3rd, Allen AA, Nwachukwu BU, Pearle A, Stein BS, Dines D, Kelly A, Kelly B, Rose H, Maynard M, Strickland S, Coleman S, Hannafin J, MacGillivray J, Marx R, Warren R, Rodeo S, Fealy S, O'Brien S, Wickiewicz T, Dines JS, Cordasco F, Altcheck D; HSS ACL Registry Group. Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction. Orthop J Sports Med 2021;9:23259671211046575. [PMID: 34671691 DOI: 10.1177/23259671211046575] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
144 Caldairou B, Foit NA, Mutti C, Fadaie F, Gill R, Lee HM, Demerath T, Urbach H, Schulze-Bonhage A, Bernasconi A, Bernasconi N. MRI-Based Machine Learning Prediction Framework to Lateralize Hippocampal Sclerosis in Patients With Temporal Lobe Epilepsy. Neurology 2021;97:e1583-93. [PMID: 34475125 DOI: 10.1212/WNL.0000000000012699] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
145 Le VNT, Kim J, Yang Y, Lee D. Evaluating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM)-Based Quality of Reports Using Convolutional Neural Network for Odontogenic Cyst and Tumor Detection. Applied Sciences 2021;11:9688. [DOI: 10.3390/app11209688] [Reference Citation Analysis]
146 Chowdhury M, Cervantes EG, Chan WY, Seitz DP. Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review. Front Psychiatry 2021;12:738466. [PMID: 34616322 DOI: 10.3389/fpsyt.2021.738466] [Reference Citation Analysis]
147 Huang C, Li SX, Caraballo C, Masoudi FA, Rumsfeld JS, Spertus JA, Normand ST, Mortazavi BJ, Krumholz HM. Performance Metrics for the Comparative Analysis of Clinical Risk Prediction Models Employing Machine Learning. Circ Cardiovasc Qual Outcomes 2021;14:e007526. [PMID: 34601947 DOI: 10.1161/CIRCOUTCOMES.120.007526] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
148 Trosko JE. In Search of a Unifying Concept in Human Diseases. Diseases 2021;9:68. [PMID: 34698126 DOI: 10.3390/diseases9040068] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
149 Khan O, Badhiwala JH, Witiw CD, Wilson JR, Fehlings MG. Machine learning algorithms for prediction of health-related quality-of-life after surgery for mild degenerative cervical myelopathy. Spine J 2021;21:1659-69. [PMID: 32045708 DOI: 10.1016/j.spinee.2020.02.003] [Cited by in Crossref: 17] [Cited by in F6Publishing: 17] [Article Influence: 8.5] [Reference Citation Analysis]
150 Machine Learning Consortium on behalf of the SPRINT Investigators. A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures. J Orthop Trauma 2021;35:e381-8. [PMID: 34533505 DOI: 10.1097/BOT.0000000000002070] [Reference Citation Analysis]
151 Azad TD, Ehresman J, Ahmed AK, Staartjes VE, Lubelski D, Stienen MN, Veeravagu A, Ratliff JK. Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery. Spine J 2021;21:1610-6. [PMID: 33065274 DOI: 10.1016/j.spinee.2020.10.006] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 8.0] [Reference Citation Analysis]
152 Anteby R, Klang E, Horesh N, Nachmany I, Shimon O, Barash Y, Kopylov U, Soffer S. Deep learning for noninvasive liver fibrosis classification: A systematic review. Liver Int 2021;41:2269-78. [PMID: 34008300 DOI: 10.1111/liv.14966] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
153 Albaradei S, Thafar M, Alsaedi A, Van Neste C, Gojobori T, Essack M, Gao X. Machine learning and deep learning methods that use omics data for metastasis prediction. Comput Struct Biotechnol J 2021;19:5008-18. [PMID: 34589181 DOI: 10.1016/j.csbj.2021.09.001] [Cited by in Crossref: 15] [Cited by in F6Publishing: 19] [Article Influence: 7.5] [Reference Citation Analysis]
154 Pouchon A, Fakra E, Haesebaert F, Legrand G, Rigon M, Schmitt E, Conus P, Bougerol T, Polosan M, Dondé C. [Early intervention in bipolar affective disorders: Why, when and how]. Encephale 2021:S0013-7006(21)00174-3. [PMID: 34565543 DOI: 10.1016/j.encep.2021.05.007] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
155 Goedmakers CMW, Lak AM, Duey AH, Senko AW, Arnaout O, Groff MW, Smith TR, Vleggeert-Lankamp CLA, Zaidi HA, Rana A, Boaro A. Deep Learning for Adjacent Segment Disease at Preoperative MRI for Cervical Radiculopathy. Radiology 2021;301:664-71. [PMID: 34546126 DOI: 10.1148/radiol.2021204731] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
156 Canares TL, Wang W, Unberath M, Clark JH. Artificial intelligence to diagnose ear disease using otoscopic image analysis: a review. J Investig Med 2021:jim-2021-001870. [PMID: 34521730 DOI: 10.1136/jim-2021-001870] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
157 Zhang G, Fu DJ, Liefers B, Faes L, Glinton S, Wagner S, Struyven R, Pontikos N, Keane PA, Balaskas K. Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study. Lancet Digit Health 2021;3:e665-75. [PMID: 34509423 DOI: 10.1016/S2589-7500(21)00134-5] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
158 Smolyansky ED, Hakeem H, Ge Z, Chen Z, Kwan P. Machine learning models for decision support in epilepsy management: A critical review. Epilepsy Behav 2021;123:108273. [PMID: 34507093 DOI: 10.1016/j.yebeh.2021.108273] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
159 Wei X, Lu Q, Jin S, Li F, Zhao Q, Cui Y, Jin S, Cao Y, Fu MR. Developing and validating a prediction model for lymphedema detection in breast cancer survivors. Eur J Oncol Nurs 2021;54:102023. [PMID: 34500318 DOI: 10.1016/j.ejon.2021.102023] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
160 Guo LL, Pfohl SR, Fries J, Posada J, Fleming SL, Aftandilian C, Shah N, Sung L. Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine. Appl Clin Inform 2021;12:808-15. [PMID: 34470057 DOI: 10.1055/s-0041-1735184] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
161 Petrosyan Y, Thavorn K, Smith G, Maclure M, Preston R, van Walravan C, Forster AJ. Predicting postoperative surgical site infection with administrative data: a random forests algorithm. BMC Med Res Methodol 2021;21:179. [PMID: 34454414 DOI: 10.1186/s12874-021-01369-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
162 Fu R, Kundu A, Mitsakakis N, Elton-Marshall T, Wang W, Hill S, Bondy SJ, Hamilton H, Selby P, Schwartz R, Chaiton MO. Machine learning applications in tobacco research: a scoping review. Tob Control 2021:tobaccocontrol-2020-056438. [PMID: 34452986 DOI: 10.1136/tobaccocontrol-2020-056438] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
163 Shelmerdine SC, Arthurs OJ, Denniston A, Sebire NJ. Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. BMJ Health Care Inform 2021;28:e100385. [PMID: 34426417 DOI: 10.1136/bmjhci-2021-100385] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
164 Stewart J, Lu J, Goudie A, Bennamoun M, Sprivulis P, Sanfillipo F, Dwivedi G. Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review. PLoS One 2021;16:e0252612. [PMID: 34428208 DOI: 10.1371/journal.pone.0252612] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
165 Gräßer F, Tesch F, Schmitt J, Abraham S, Malberg H, Zaunseder S. A pharmaceutical therapy recommender system enabling shared decision-making. User Model User-Adap Inter. [DOI: 10.1007/s11257-021-09298-4] [Reference Citation Analysis]
166 Speiser JL, Callahan KE, Houston DK, Fanning J, Gill TM, Guralnik JM, Newman AB, Pahor M, Rejeski WJ, Miller ME. Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults. J Gerontol A Biol Sci Med Sci 2021;76:647-54. [PMID: 32498077 DOI: 10.1093/gerona/glaa138] [Cited by in Crossref: 13] [Cited by in F6Publishing: 15] [Article Influence: 6.5] [Reference Citation Analysis]
167 Kwong JCC, McLoughlin LC, Haider M, Goldenberg MG, Erdman L, Rickard M, Lorenzo AJ, Hung AJ, Farcas M, Goldenberg L, Nguan C, Braga LH, Mamdani M, Goldenberg A, Kulkarni GS. Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework. Eur Urol Focus 2021;7:672-82. [PMID: 34362709 DOI: 10.1016/j.euf.2021.07.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
168 Hill BL, Rakocz N, Rudas Á, Chiang JN, Wang S, Hofer I, Cannesson M, Halperin E. Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning. Sci Rep 2021;11:15755. [PMID: 34344934 DOI: 10.1038/s41598-021-94913-y] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
169 Groot OQ, Bindels BJJ, Ogink PT, Kapoor ND, Twining PK, Collins AK, Bongers MER, Lans A, Oosterhoff JHF, Karhade AV, Verlaan JJ, Schwab JH. Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review. Acta Orthop 2021;92:385-93. [PMID: 33870837 DOI: 10.1080/17453674.2021.1910448] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
170 Harrison CJ, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction to natural language processing. BMC Med Res Methodol 2021;21:158. [PMID: 34332525 DOI: 10.1186/s12874-021-01347-1] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
171 Walsh I, Fishman D, Garcia-Gasulla D, Titma T, Pollastri G, Harrow J, Psomopoulos FE, Tosatto SCE; ELIXIR Machine Learning Focus Group. DOME: recommendations for supervised machine learning validation in biology. Nat Methods 2021. [PMID: 34316068 DOI: 10.1038/s41592-021-01205-4] [Cited by in Crossref: 39] [Cited by in F6Publishing: 43] [Article Influence: 19.5] [Reference Citation Analysis]
172 Forte C, Voinea A, Chichirau M, Yeshmagambetova G, Albrecht LM, Erfurt C, Freundt LA, Carmo LOE, Henning RH, van der Horst ICC, Sundelin T, Wiering MA, Axelsson J, Epema AH. Deep Learning for Identification of Acute Illness and Facial Cues of Illness. Front Med (Lausanne) 2021;8:661309. [PMID: 34381793 DOI: 10.3389/fmed.2021.661309] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
173 Lu JH, Callahan A, Patel BS, Morse KE, Dash D, Shah NH. Low adherence to existing model reporting guidelines by commonly used clinical prediction models.. [DOI: 10.1101/2021.07.21.21260282] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
174 Zhao J, Zhang W, Zhu YY, Zheng HY, Xu L, Zhang J, Liu SY, Li FY, Song B. Development and Validation of Noninvasive MRI-Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma. J Magn Reson Imaging 2021. [PMID: 34296802 DOI: 10.1002/jmri.27846] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
175 Katakam A, Karhade AV, Collins A, Shin D, Bragdon C, Chen AF, Melnic CM, Schwab JH, Bedair HS. Development of machine learning algorithms to predict achievement of minimal clinically important difference for the KOOS-PS following total knee arthroplasty. J Orthop Res 2021. [PMID: 34275163 DOI: 10.1002/jor.25125] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
176 Zhao H, You J, Peng Y, Feng Y. Machine Learning Algorithm Using Electronic Chart-Derived Data to Predict Delirium After Elderly Hip Fracture Surgeries: A Retrospective Case-Control Study. Front Surg 2021;8:634629. [PMID: 34327210 DOI: 10.3389/fsurg.2021.634629] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
177 Andaur Navarro CL, Damen JAA, Takada T, Nijman SWJ, Dhiman P, Ma J, Collins GS, Bajpai R, Riley RD, Moons KG, Hooft L. Completeness of reporting of clinical prediction models developed using supervised machine learning: A systematic review.. [DOI: 10.1101/2021.06.28.21259089] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
178 Fan G, Liu H, Yang S, Luo L, Wang L, Pang M, Liu B, Zhang L, Han L, Rong L. Discharge prediction of critical patients with spinal cord injury: a machine learning study with 1485 cases.. [DOI: 10.1101/2021.06.26.21259569] [Reference Citation Analysis]
179 Arabi Belaghi R, Beyene J, McDonald SD. Prediction of preterm birth in nulliparous women using logistic regression and machine learning. PLoS One 2021;16:e0252025. [PMID: 34191801 DOI: 10.1371/journal.pone.0252025] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
180 Dhiman P, Ma J, Navarro CA, Speich B, Bullock G, Damen JA, Kirtley S, Hooft L, Riley RD, Van Calster B, Moons KGM, Collins GS. Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. J Clin Epidemiol 2021;138:60-72. [PMID: 34214626 DOI: 10.1016/j.jclinepi.2021.06.024] [Cited by in Crossref: 16] [Cited by in F6Publishing: 19] [Article Influence: 8.0] [Reference Citation Analysis]
181 George N, Moseley E, Eber R, Siu J, Samuel M, Yam J, Huang K, Celi LA, Lindvall C. Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation. PLoS One 2021;16:e0253443. [PMID: 34185798 DOI: 10.1371/journal.pone.0253443] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
182 Sufriyana H, Wu Y, Su EC. Prognostication for prelabor rupture of membranes and the time of delivery in nationwide insured women: development, validation, and deployment.. [DOI: 10.1101/2021.06.16.21258884] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
183 Barrachina-Fernández M, Maitín AM, Sánchez-Ávila C, Romero JP. Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges. Sensors (Basel) 2021;21:4188. [PMID: 34207198 DOI: 10.3390/s21124188] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
184 Kunze KN, Polce EM, Clapp I, Nwachukwu BU, Chahla J, Nho SJ. Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes. J Bone Joint Surg Am 2021;103:1055-62. [PMID: 33877058 DOI: 10.2106/JBJS.20.01640] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 10.0] [Reference Citation Analysis]
185 Weaver CGW, Basmadjian RB, Williamson T, Mcbrien K, Sajobi T, Boyne D, Yusuf M, Ronksley PE. Reporting of Model Performance and Statistical Methods in Studies That Use Machine Learning to Develop Clinical Prediction Models: Protocol for a Systematic Review (Preprint).. [DOI: 10.2196/preprints.30956] [Reference Citation Analysis]
186 Kino S, Hsu YT, Shiba K, Chien YS, Mita C, Kawachi I, Daoud A. A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects. SSM Popul Health 2021;15:100836. [PMID: 34169138 DOI: 10.1016/j.ssmph.2021.100836] [Cited by in Crossref: 12] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
187 Karhade AV, Shin D, Florissi I, Schwab JH. Development of predictive algorithms for length of stay greater than one day after one- or two-level anterior cervical discectomy and fusion. Seminars in Spine Surgery 2021;33:100874. [DOI: 10.1016/j.semss.2021.100874] [Reference Citation Analysis]
188 Polce EM, Kunze KN, Fu MC, Garrigues GE, Forsythe B, Nicholson GP, Cole BJ, Verma NN. Development of supervised machine learning algorithms for prediction of satisfaction at 2 years following total shoulder arthroplasty. J Shoulder Elbow Surg 2021;30:e290-9. [PMID: 33010437 DOI: 10.1016/j.jse.2020.09.007] [Cited by in Crossref: 22] [Cited by in F6Publishing: 21] [Article Influence: 11.0] [Reference Citation Analysis]
189 Calderaro J, Kather JN. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut 2021;70:1183-93. [PMID: 33214163 DOI: 10.1136/gutjnl-2020-322880] [Cited by in Crossref: 35] [Cited by in F6Publishing: 34] [Article Influence: 17.5] [Reference Citation Analysis]
190 Adlung L, Cohen Y, Mor U, Elinav E. Machine learning in clinical decision making. Med 2021;2:642-65. [DOI: 10.1016/j.medj.2021.04.006] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 5.5] [Reference Citation Analysis]
191 Verdonck M, Carvalho H, Berghmans J, Forget P, Poelaert J. Exploratory Outlier Detection for Acceleromyographic Neuromuscular Monitoring: Machine Learning Approach. J Med Internet Res 2021;23:e25913. [PMID: 34152273 DOI: 10.2196/25913] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
192 Kunze KN, Polce EM, Alter TD, Nho SJ. Machine Learning Algorithms Predict Prolonged Opioid Use in Opioid-Naïve Primary Hip Arthroscopy Patients. J Am Acad Orthop Surg Glob Res Rev 2021;5:e21.00093-8. [PMID: 34032690 DOI: 10.5435/JAAOSGlobal-D-21-00093] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
193 Zhong J, Si L, Zhang G, Huo J, Xing Y, Hu Y, Zhang H, Yao W. Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis. Syst Rev 2021;10:149. [PMID: 34006309 DOI: 10.1186/s13643-021-01683-9] [Reference Citation Analysis]
194 He B, Chen W, Liu L, Hou Z, Zhu H, Cheng H, Zhang Y, Zhan S, Wang S. Prediction Models for Prognosis of Cervical Cancer: Systematic Review and Critical Appraisal. Front Public Health 2021;9:654454. [PMID: 34026714 DOI: 10.3389/fpubh.2021.654454] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
195 Khan O, Badhiwala JH, Akbar MA, Fehlings MG. Prediction of Worse Functional Status After Surgery for Degenerative Cervical Myelopathy: A Machine Learning Approach. Neurosurgery 2021;88:584-91. [PMID: 33289519 DOI: 10.1093/neuros/nyaa477] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
196 Staron M, Herges HO, Naredi S, Block L, El-merhi A, Vithal R, Elam M. Robust Machine Learning in Critical Care — Software Engineering and Medical Perspectives. 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) 2021. [DOI: 10.1109/wain52551.2021.00016] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
197 Kunze KN, Polce EM, Nwachukwu BU, Chahla J, Nho SJ. Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy. Arthroscopy 2021;37:1488-97. [PMID: 33460708 DOI: 10.1016/j.arthro.2021.01.005] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 9.5] [Reference Citation Analysis]
198 Lu Y, Khazi ZM, Agarwalla A, Forsythe B, Taunton MJ. Development of a Machine Learning Algorithm to Predict Nonroutine Discharge Following Unicompartmental Knee Arthroplasty. J Arthroplasty 2021;36:1568-76. [PMID: 33358514 DOI: 10.1016/j.arth.2020.12.003] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
199 Carnero-Pardo C, López-Alcalde S, Florido-Santiago M, Espinosa-García M, Rego-García I, Calle-Calle R, Carrera-Muñoz I, de la Vega-Cotarelo R. Diagnostic accuracy and predictive validity of associated use of Fototest and Mini-Cog in cognitive impairment. Neurologia (Engl Ed) 2021:S0213-4853(21)00052-9. [PMID: 33896655 DOI: 10.1016/j.nrl.2021.01.017] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
200 O'Shea RJ, Sharkey AR, Cook GJR, Goh V. Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis. Eur Radiol 2021. [PMID: 33860829 DOI: 10.1007/s00330-021-07881-2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
201 ZhuParris A, Kruizinga MD, van Gent M, Dessing E, Exadaktylos V, Doll RJ, Stuurman FE, Driessen GA, Cohen AF. Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm. Front Pediatr 2021;9:651356. [PMID: 33928059 DOI: 10.3389/fped.2021.651356] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
202 Zhao X, Liao K, Wang W, Xu J, Meng L. Can a deep learning model based on intraoperative time-series monitoring data predict post-hysterectomy quality of recovery? Perioper Med (Lond) 2021;10:8. [PMID: 33820562 DOI: 10.1186/s13741-021-00178-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
203 Feng C, Zhou S, Qu Y, Wang Q, Bao S, Li Y, Yang T. Overview of Artificial Intelligence Applications in Chinese Medicine Therapy. Evid Based Complement Alternat Med 2021;2021:6678958. [PMID: 33815559 DOI: 10.1155/2021/6678958] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
204 Kunze KN, Polce EM, Rasio J, Nho SJ. Machine Learning Algorithms Predict Clinically Significant Improvements in Satisfaction After Hip Arthroscopy. Arthroscopy 2021;37:1143-51. [PMID: 33359160 DOI: 10.1016/j.arthro.2020.11.027] [Cited by in Crossref: 28] [Cited by in F6Publishing: 25] [Article Influence: 14.0] [Reference Citation Analysis]
205 Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review. J Med Internet Res 2021;23:e22394. [PMID: 33792552 DOI: 10.2196/22394] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
206 Ghaednia H, Lans A, Sauder N, Shin D, Grant WG, Chopra RR, Oosterhoff JH, Fourman MS, Schwab JH, Tobert DG. Deep learning in spine surgery. Seminars in Spine Surgery 2021;33:100876. [DOI: 10.1016/j.semss.2021.100876] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
207 Calabrese F, Pezzuto F, Fortarezza F, Boscolo A, Lunardi F, Giraudo C, Cattelan A, Del Vecchio C, Lorenzoni G, Vedovelli L, Sella N, Rossato M, Rea F, Vettor R, Plebani M, Cozzi E, Crisanti A, Navalesi P, Gregori D. Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure. J Pathol 2021;254:173-84. [PMID: 33626204 DOI: 10.1002/path.5653] [Cited by in Crossref: 19] [Cited by in F6Publishing: 19] [Article Influence: 9.5] [Reference Citation Analysis]
208 Alabi RO, Youssef O, Pirinen M, Elmusrati M, Mäkitie AA, Leivo I, Almangush A. Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review. Artif Intell Med 2021;115:102060. [PMID: 34001326 DOI: 10.1016/j.artmed.2021.102060] [Cited by in Crossref: 17] [Cited by in F6Publishing: 21] [Article Influence: 8.5] [Reference Citation Analysis]
209 Kundu A, Chaiton M, Billington R, Grace D, Fu R, Logie C, Baskerville B, Yager C, Mitsakakis N, Schwartz R. Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review (Preprint).. [DOI: 10.2196/preprints.28962] [Reference Citation Analysis]
210 Machine Learning Consortium, on behalf of the SPRINT and FLOW Investigators. A Machine Learning Algorithm to Identify Patients with Tibial Shaft Fractures at Risk for Infection After Operative Treatment. J Bone Joint Surg Am 2021;103:532-40. [PMID: 33394819 DOI: 10.2106/JBJS.20.00903] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
211 Zhao J, Zhang W, Fan CL, Zhang J, Yuan F, Liu SY, Li FY, Song B. Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: A pilot study. Eur J Radiol 2021;138:109631. [PMID: 33711571 DOI: 10.1016/j.ejrad.2021.109631] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
212 Bhambhvani HP, Zamora A, Shkolyar E, Prado K, Greenberg DR, Kasman AM, Liao J, Shah S, Srinivas S, Skinner EC, Shah JB. Development of robust artificial neural networks for prediction of 5-year survival in bladder cancer. Urologic Oncology: Seminars and Original Investigations 2021;39:193.e7-193.e12. [DOI: 10.1016/j.urolonc.2020.05.009] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
213 Wang Q, Zhu H. Letter to the Editor: Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction? Clin Orthop Relat Res 2021;479:634-5. [PMID: 33394761 DOI: 10.1097/CORR.0000000000001631] [Reference Citation Analysis]
214 Polce EM, Kunze KN, Paul KM, Levine BR. Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty. Arthroplast Today 2021;8:268-277.e2. [PMID: 34095403 DOI: 10.1016/j.artd.2021.01.006] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
215 Hu M, Shu X, Yu G, Wu X, Välimäki M, Feng H. A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study. J Med Internet Res 2021;23:e20298. [PMID: 33625369 DOI: 10.2196/20298] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
216 Brisk R, Bond R, Finlay D, McLaughlin J, Piadlo A, Leslie SJ, Gossman DE, Menown IB, McEneaney DJ, Warren S. The effect of confounding data features on a deep learning algorithm to predict complete coronary occlusion in a retrospective observational setting. Eur Heart J Digit Health 2021;2:127-34. [PMID: 36711180 DOI: 10.1093/ehjdh/ztab002] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
217 Honoré H, Gade R, Nielsen JF, Mechlenburg I. Developing and validating an accelerometer-based algorithm with machine learning to classify physical activity after acquired brain injury. Brain Inj 2021;35:460-7. [PMID: 33599161 DOI: 10.1080/02699052.2021.1880026] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
218 Brnabic A, Hess LM. Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making. BMC Med Inform Decis Mak 2021;21:54. [PMID: 33588830 DOI: 10.1186/s12911-021-01403-2] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 8.5] [Reference Citation Analysis]
219 Saw SN, Biswas A, Mattar CNZ, Lee HK, Yap CH. Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor. Prenat Diagn 2021;41:505-16. [PMID: 33462877 DOI: 10.1002/pd.5903] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
220 Stevens LM, Linstead E, Hall JL, Kao DP. Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS. Circ Heart Fail 2021;14:e006799. [PMID: 33557575 DOI: 10.1161/CIRCHEARTFAILURE.119.006799] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 7.0] [Reference Citation Analysis]
221 Saboonchi H, Blanchette D, Hayes K. Advancements in Radiographic Evaluation Through the Migration into NDE 4.0. J Nondestr Eval 2021;40:17. [PMID: 33518876 DOI: 10.1007/s10921-021-00749-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
222 Loher P, Karathanasis N. Machine Learning Approaches Identify Genes Containing Spatial Information From Single-Cell Transcriptomics Data. Front Genet 2020;11:612840. [PMID: 33633771 DOI: 10.3389/fgene.2020.612840] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
223 Kleppe A, Skrede OJ, De Raedt S, Liestøl K, Kerr DJ, Danielsen HE. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer 2021;21:199-211. [PMID: 33514930 DOI: 10.1038/s41568-020-00327-9] [Cited by in Crossref: 60] [Cited by in F6Publishing: 63] [Article Influence: 30.0] [Reference Citation Analysis]
224 Pethani F. Promises and perils of artificial intelligence in dentistry. Aust Dent J 2021;66:124-35. [PMID: 33340123 DOI: 10.1111/adj.12812] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
225 Lee CK, Samad M, Hofer I, Cannesson M, Baldi P. Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality. NPJ Digit Med 2021;4:8. [PMID: 33420341 DOI: 10.1038/s41746-020-00377-1] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
226 Haymond S, Julian RK, Gill EL, Master SR. Machine learning and big data in pediatric laboratory medicine. Biochemical and Molecular Basis of Pediatric Disease 2021. [DOI: 10.1016/b978-0-12-817962-8.00018-4] [Reference Citation Analysis]
227 Cychnerski J, Dziubich T. Process of Medical Dataset Construction for Machine Learning - Multifield Study and Guidelines. New Trends in Database and Information Systems 2021. [DOI: 10.1007/978-3-030-85082-1_20] [Reference Citation Analysis]
228 Bracher-Smith M, Crawford K, Escott-Price V. Machine learning for genetic prediction of psychiatric disorders: a systematic review. Mol Psychiatry 2021;26:70-9. [PMID: 32591634 DOI: 10.1038/s41380-020-0825-2] [Cited by in Crossref: 42] [Cited by in F6Publishing: 43] [Article Influence: 21.0] [Reference Citation Analysis]
229 Dee EC, Yu RC, Celi LA, Nehal US. AIM and Business Models of Healthcare. Artificial Intelligence in Medicine 2021. [DOI: 10.1007/978-3-030-58080-3_247-1] [Reference Citation Analysis]
230 Guo K, Fu X, Zhang H, Wang M, Hong S, Ma S. Predicting the postoperative blood coagulation state of children with congenital heart disease by machine learning based on real-world data. Transl Pediatr 2021;10:33-43. [PMID: 33633935 DOI: 10.21037/tp-20-238] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
231 Iorfino F, Ho N, Carpenter JS, Cross SP, Davenport TA, Hermens DF, Yee H, Nichles A, Zmicerevska N, Guastella A, Scott E, Hickie IB. Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study. PLoS One 2020;15:e0243467. [PMID: 33382713 DOI: 10.1371/journal.pone.0243467] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
232 Sax DR, Mark DG, Huang J, Sofrygin O, Rana JS, Collins SP, Storrow AB, Liu D, Reed ME. Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure. Ann Emerg Med 2021;77:237-48. [PMID: 33349492 DOI: 10.1016/j.annemergmed.2020.09.436] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 3.7] [Reference Citation Analysis]
233 Khan O, Badhiwala JH, Grasso G, Fehlings MG. Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care. World Neurosurg 2020;140:512-8. [PMID: 32797983 DOI: 10.1016/j.wneu.2020.04.022] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 3.7] [Reference Citation Analysis]
234 Adil SM, Elahi C, Gramer R, Spears CA, Fuller AT, Haglund MM, Dunn TW. Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda. J Neurotrauma 2021;38:928-39. [PMID: 33054545 DOI: 10.1089/neu.2020.7262] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 3.7] [Reference Citation Analysis]
235 van der Ven WH, Veelo DP, Wijnberge M, van der Ster BJP, Vlaar APJ, Geerts BF. One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making. Surgery 2021;169:1300-3. [PMID: 33309616 DOI: 10.1016/j.surg.2020.09.041] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 1.7] [Reference Citation Analysis]
236 Douville NJ, Douville CB, Mentz G, Mathis MR, Pancaro C, Tremper KK, Engoren M. Clinically applicable approach for predicting mechanical ventilation in patients with COVID-19. Br J Anaesth 2021;126:578-89. [PMID: 33454051 DOI: 10.1016/j.bja.2020.11.034] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 2.3] [Reference Citation Analysis]
237 Maitín AM, García-tejedor AJ, Muñoz JPR. Machine Learning Approaches for Detecting Parkinson’s Disease from EEG Analysis: A Systematic Review. Applied Sciences 2020;10:8662. [DOI: 10.3390/app10238662] [Cited by in Crossref: 19] [Cited by in F6Publishing: 19] [Article Influence: 6.3] [Reference Citation Analysis]
238 Karhade AV, Schwab JH. CORR Synthesis: When Should We Be Skeptical of Clinical Prediction Models? Clin Orthop Relat Res 2020;478:2722-8. [PMID: 32667756 DOI: 10.1097/CORR.0000000000001367] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 4.7] [Reference Citation Analysis]
239 Liu Y, Qu H, Wenocur AS, Qu J, Chang X, Glessner J, Sleiman P, Tian L, Hakonarson H. Interpretation of Maturity-Onset Diabetes of the Young Genetic Variants Based on American College of Medical Genetics and Genomics Criteria: Machine-Learning Model Development. JMIR Biomed Eng 2020;5:e20506. [DOI: 10.2196/20506] [Reference Citation Analysis]
240 Spence J, Mazer CD. The Future Directions of Research in Cardiac Anesthesiology. Adv Anesth 2020;38:269-82. [PMID: 34106839 DOI: 10.1016/j.aan.2020.09.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
241 Kennedy-Metz LR, Mascagni P, Torralba A, Dias RD, Perona P, Shah JA, Padoy N, Zenati MA. Computer Vision in the Operating Room: Opportunities and Caveats. IEEE Trans Med Robot Bionics 2021;3:2-10. [PMID: 33644703 DOI: 10.1109/tmrb.2020.3040002] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
242 Verdonck M, Carvalho H, Berghmans J, Forget P, Poelaert J. Exploratory Outlier Detection for Acceleromyographic Neuromuscular Monitoring: Machine Learning Approach (Preprint).. [DOI: 10.2196/preprints.25913] [Reference Citation Analysis]
243 Bhambhvani HP, Zamora A, Velaer K, Greenberg DR, Sheth KR. Deep learning enabled prediction of 5-year survival in pediatric genitourinary rhabdomyosarcoma. Surg Oncol 2021;36:23-7. [PMID: 33276260 DOI: 10.1016/j.suronc.2020.11.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
244 Sufriyana H, Husnayain A, Chen YL, Kuo CY, Singh O, Yeh TY, Wu YW, Su EC. Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis. JMIR Med Inform 2020;8:e16503. [PMID: 33200995 DOI: 10.2196/16503] [Cited by in Crossref: 20] [Cited by in F6Publishing: 21] [Article Influence: 6.7] [Reference Citation Analysis]
245 Mordaunt DA. On Clinical Utility and Systematic Reporting in Case Studies of Healthcare Process Mining. Int J Environ Res Public Health 2020;17:E8298. [PMID: 33182679 DOI: 10.3390/ijerph17228298] [Reference Citation Analysis]
246 Castillo-Sánchez G, Marques G, Dorronzoro E, Rivera-Romero O, Franco-Martín M, De la Torre-Díez I. Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review. J Med Syst 2020;44:205. [PMID: 33165729 DOI: 10.1007/s10916-020-01669-5] [Cited by in Crossref: 16] [Cited by in F6Publishing: 20] [Article Influence: 5.3] [Reference Citation Analysis]
247 Howard FM, Kochanny S, Koshy M, Spiotto M, Pearson AT. Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer. JAMA Netw Open 2020;3:e2025881. [PMID: 33211108 DOI: 10.1001/jamanetworkopen.2020.25881] [Cited by in Crossref: 18] [Cited by in F6Publishing: 21] [Article Influence: 6.0] [Reference Citation Analysis]
248 Buchlak QD, Esmaili N, Leveque J, Bennett C, Piccardi M, Farrokhi F. Ethical thinking machines in surgery and the requirement for clinical leadership. The American Journal of Surgery 2020;220:1372-4. [DOI: 10.1016/j.amjsurg.2020.06.073] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
249 Morgenstern JD, Buajitti E, O'Neill M, Piggott T, Goel V, Fridman D, Kornas K, Rosella LC. Predicting population health with machine learning: a scoping review. BMJ Open 2020;10:e037860. [PMID: 33109649 DOI: 10.1136/bmjopen-2020-037860] [Cited by in Crossref: 21] [Cited by in F6Publishing: 22] [Article Influence: 7.0] [Reference Citation Analysis]
250 Serviá L, Montserrat N, Badia M, Llompart-Pou JA, Barea-Mendoza JA, Chico-Fernández M, Sánchez-Casado M, Jiménez JM, Mayor DM, Trujillano J. Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study. BMC Med Res Methodol 2020;20:262. [PMID: 33081694 DOI: 10.1186/s12874-020-01151-3] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
251 Lu Y, Forlenza E, Cohn MR, Lavoie-gagne O, Wilbur RR, Song BM, Krych AJ, Forsythe B. Machine learning can reliably identify patients at risk of overnight hospital admission following anterior cruciate ligament reconstruction. Knee Surg Sports Traumatol Arthrosc 2021;29:2958-66. [DOI: 10.1007/s00167-020-06321-w] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
252 Sampa MB, Hossain MN, Hoque MR, Islam R, Yokota F, Nishikitani M, Ahmed A. Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison. JMIR Med Inform 2020;8:e18331. [PMID: 33030442 DOI: 10.2196/18331] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
253 Sapir-Pichhadze R, Kaplan B. Seeing the Forest for the Trees: Random Forest Models for Predicting Survival in Kidney Transplant Recipients. Transplantation 2020;104:905-6. [PMID: 31403553 DOI: 10.1097/TP.0000000000002923] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
254 Shivabalan KR, Deb B, Goel S, Arivan R. SMORASO-DT : A hybrid machine learning classification model to classify individuals based on working memory load in mental arithmetic task.. [DOI: 10.1101/2020.10.02.20205922] [Reference Citation Analysis]
255 Kocak B, Kus EA, Kilickesmez O. How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts. Eur Radiol 2021;31:1819-30. [PMID: 33006018 DOI: 10.1007/s00330-020-07324-4] [Cited by in Crossref: 17] [Cited by in F6Publishing: 20] [Article Influence: 5.7] [Reference Citation Analysis]
256 Yu D, Williams GW, Aguilar D, Yamal JM, Maroufy V, Wang X, Zhang C, Huang Y, Gu Y, Talebi Y, Wu H. Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients. Ann Clin Transl Neurol 2020;7:2178-85. [PMID: 32990362 DOI: 10.1002/acn3.51208] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
257 Thomsen K, Christensen AL, Iversen L, Lomholt HB, Winther O. Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases. Front Med (Lausanne) 2020;7:574329. [PMID: 33072786 DOI: 10.3389/fmed.2020.574329] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
258 Higaki A, Uetani T, Ikeda S, Yamaguchi O. Co-authorship network analysis in cardiovascular research utilizing machine learning (2009-2019). Int J Med Inform 2020;143:104274. [PMID: 32987350 DOI: 10.1016/j.ijmedinf.2020.104274] [Cited by in Crossref: 18] [Cited by in F6Publishing: 20] [Article Influence: 6.0] [Reference Citation Analysis]
259 Kim DW, Jang HY, Ko Y, Son JH, Kim PH, Kim SO, Lim JS, Park SH. Inconsistency in the use of the term "validation" in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging. PLoS One 2020;15:e0238908. [PMID: 32915901 DOI: 10.1371/journal.pone.0238908] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
260 Silva K, Lee WK, Forbes A, Demmer RT, Barton C, Enticott J. Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis. Int J Med Inform 2020;143:104268. [PMID: 32950874 DOI: 10.1016/j.ijmedinf.2020.104268] [Cited by in Crossref: 25] [Cited by in F6Publishing: 14] [Article Influence: 8.3] [Reference Citation Analysis]
261 Zhong J, Hu Y, Si L, Jia G, Xing Y, Zhang H, Yao W. A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol 2021;31:1526-35. [PMID: 32876837 DOI: 10.1007/s00330-020-07221-w] [Cited by in Crossref: 24] [Cited by in F6Publishing: 25] [Article Influence: 8.0] [Reference Citation Analysis]
262 Wu G, Woodruff HC, Chatterjee A, Lambin P. Reply to "COVID-19 prediction models should adhere to methodological and reporting standards". Eur Respir J 2020;56:2002918. [PMID: 32817260 DOI: 10.1183/13993003.02918-2020] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
263 Futoma J, Simons M, Panch T, Doshi-Velez F, Celi LA. The myth of generalisability in clinical research and machine learning in health care. Lancet Digit Health 2020;2:e489-92. [PMID: 32864600 DOI: 10.1016/S2589-7500(20)30186-2] [Cited by in Crossref: 102] [Cited by in F6Publishing: 112] [Article Influence: 34.0] [Reference Citation Analysis]
264 Bey R, Goussault R, Grolleau F, Benchoufi M, Porcher R. Fold-stratified cross-validation for unbiased and privacy-preserving federated learning. J Am Med Inform Assoc 2020;27:1244-51. [PMID: 32620945 DOI: 10.1093/jamia/ocaa096] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
265 Zhang B, Yu K, Ning Z, Wang K, Dong Y, Liu X, Liu S, Wang J, Zhu C, Yu Q, Duan Y, Lv S, Zhang X, Chen Y, Wang X, Shen J, Peng J, Chen Q, Zhang Y, Zhang X, Zhang S. Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study. Bone 2020;140:115561. [PMID: 32730939 DOI: 10.1016/j.bone.2020.115561] [Cited by in Crossref: 25] [Cited by in F6Publishing: 27] [Article Influence: 8.3] [Reference Citation Analysis]
266 Kopitar L, Kocbek P, Cilar L, Sheikh A, Stiglic G. Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Sci Rep 2020;10:11981. [PMID: 32686721 DOI: 10.1038/s41598-020-68771-z] [Cited by in Crossref: 68] [Cited by in F6Publishing: 74] [Article Influence: 22.7] [Reference Citation Analysis]
267 Coleman BC, Fodeh S, Lisi AJ, Goulet JL, Corcoran KL, Bathulapalli H, Brandt CA. Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization. Chiropr Man Therap 2020;28:47. [PMID: 32680545 DOI: 10.1186/s12998-020-00335-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
268 Groezinger M, Huppert D, Strobl R, Grill E. Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry. J Neurol 2020;267:160-7. [PMID: 32661715 DOI: 10.1007/s00415-020-10061-9] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
269 Burns ML, Mathis MR, Vandervest J, Tan X, Lu B, Colquhoun DA, Shah N, Kheterpal S, Saager L. Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning. Anesthesiology 2020;132:738-49. [PMID: 32028374 DOI: 10.1097/ALN.0000000000003150] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
270 Schultebraucks K, Shalev AY, Michopoulos V, Grudzen CR, Shin SM, Stevens JS, Maples-Keller JL, Jovanovic T, Bonanno GA, Rothbaum BO, Marmar CR, Nemeroff CB, Ressler KJ, Galatzer-Levy IR. A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nat Med 2020;26:1084-8. [PMID: 32632194 DOI: 10.1038/s41591-020-0951-z] [Cited by in Crossref: 55] [Cited by in F6Publishing: 59] [Article Influence: 18.3] [Reference Citation Analysis]
271 Anderson AB, Grazal CF, Balazs GC, Potter BK, Dickens JF, Forsberg JA. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction? Clin Orthop Relat Res 2020;478:0-1618. [PMID: 32282466 DOI: 10.1097/CORR.0000000000001251] [Cited by in Crossref: 19] [Cited by in F6Publishing: 18] [Article Influence: 6.3] [Reference Citation Analysis]
272 Mathis MR, Engoren MC, Joo H, Maile MD, Aaronson KD, Burns ML, Sjoding MW, Douville NJ, Janda AM, Hu Y, Najarian K, Kheterpal S. Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach. Anesth Analg 2020;130:1188-200. [PMID: 32287126 DOI: 10.1213/ANE.0000000000004630] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
273 Cherifa M, Blet A, Chambaz A, Gayat E, Resche-Rigon M, Pirracchio R. Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm. Anesth Analg 2020;130:1157-66. [PMID: 32287123 DOI: 10.1213/ANE.0000000000004539] [Cited by in Crossref: 23] [Cited by in F6Publishing: 23] [Article Influence: 7.7] [Reference Citation Analysis]
274 Bahl M. Artificial Intelligence: A Primer for Breast Imaging Radiologists. J Breast Imaging 2020;2:304-14. [PMID: 32803154 DOI: 10.1093/jbi/wbaa033] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 3.3] [Reference Citation Analysis]
275 Weenk M, Bredie SJ, Koeneman M, Hesselink G, van Goor H, van de Belt TH. Continuous Monitoring of Vital Signs in the General Ward Using Wearable Devices: Randomized Controlled Trial. J Med Internet Res 2020;22:e15471. [PMID: 32519972 DOI: 10.2196/15471] [Cited by in Crossref: 33] [Cited by in F6Publishing: 34] [Article Influence: 11.0] [Reference Citation Analysis]
276 Unberath P, Prokosch HU, Gründner J, Erpenbeck M, Maier C, Christoph J. EHR-Independent Predictive Decision Support Architecture Based on OMOP. Appl Clin Inform 2020;11:399-404. [PMID: 32492716 DOI: 10.1055/s-0040-1710393] [Cited by in Crossref: 11] [Cited by in F6Publishing: 5] [Article Influence: 3.7] [Reference Citation Analysis]
277 Schultebraucks K, Qian M, Abu-Amara D, Dean K, Laska E, Siegel C, Gautam A, Guffanti G, Hammamieh R, Misganaw B, Mellon SH, Wolkowitz OM, Blessing EM, Etkin A, Ressler KJ, Doyle FJ 3rd, Jett M, Marmar CR. Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors. Mol Psychiatry 2020. [PMID: 32488126 DOI: 10.1038/s41380-020-0789-2] [Cited by in Crossref: 19] [Cited by in F6Publishing: 28] [Article Influence: 6.3] [Reference Citation Analysis]
278 Karhade AV, Cha TD, Fogel HA, Hershman SH, Tobert DG, Schoenfeld AJ, Bono CM, Schwab JH. Predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients. Spine J 2020;20:888-95. [PMID: 31901553 DOI: 10.1016/j.spinee.2019.12.019] [Cited by in Crossref: 34] [Cited by in F6Publishing: 33] [Article Influence: 11.3] [Reference Citation Analysis]
279 Lonsdale H, Jalali A, Ahumada L, Matava C. Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care. The Journal of Pediatrics 2020;221:S3-S10. [DOI: 10.1016/j.jpeds.2020.02.039] [Cited by in Crossref: 16] [Cited by in F6Publishing: 19] [Article Influence: 5.3] [Reference Citation Analysis]
280 Sufriyana H, Wu YW, Su EC. Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort. JMIR Med Inform 2020;8:e15411. [PMID: 32348266 DOI: 10.2196/15411] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 3.7] [Reference Citation Analysis]
281 Hu M, Shu X, Yu G, Wu X, Välimäki M, Feng H. A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study (Preprint).. [DOI: 10.2196/preprints.20298] [Reference Citation Analysis]
282 De la Garza-Salazar F, Romero-Ibarguengoitia ME, Rodriguez-Diaz EA, Azpiri-Lopez JR, González-Cantu A. Improvement of electrocardiographic diagnostic accuracy of left ventricular hypertrophy using a Machine Learning approach. PLoS One 2020;15:e0232657. [PMID: 32401764 DOI: 10.1371/journal.pone.0232657] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
283 Roth JA, Radevski G, Marzolini C, Rauch A, Günthard HF, Kouyos RD, Fux CA, Scherrer AU, Calmy A, Cavassini M, Kahlert CR, Bernasconi E, Bogojeska J, Battegay M. Cohort-derived machine learning models for individual prediction of chronic kidney disease in people living with HIV: a prospective multicentre cohort study. J Infect Dis 2020:jiaa236. [PMID: 32386061 DOI: 10.1093/infdis/jiaa236] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
284 Grados D, García S, Schrevens E. Assessing the potato yield gap in the Peruvian Central Andes. Agricultural Systems 2020;181:102817. [DOI: 10.1016/j.agsy.2020.102817] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
285 Fatima N, Zheng H, Massaad E, Hadzipasic M, Shankar GM, Shin JH. Development and Validation of Machine Learning Algorithms for Predicting Adverse Events After Surgery for Lumbar Degenerative Spondylolisthesis. World Neurosurg 2020;140:627-41. [PMID: 32344139 DOI: 10.1016/j.wneu.2020.04.135] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
286 Hofer IS, Lee C, Gabel E, Baldi P, Cannesson M. Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set. NPJ Digit Med 2020;3:58. [PMID: 32352036 DOI: 10.1038/s41746-020-0248-0] [Cited by in Crossref: 18] [Cited by in F6Publishing: 20] [Article Influence: 6.0] [Reference Citation Analysis]
287 Sufriyana H, Wu YW, Su EC. Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia. EBioMedicine 2020;54:102710. [PMID: 32283530 DOI: 10.1016/j.ebiom.2020.102710] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 4.7] [Reference Citation Analysis]
288 Basu S, Faghmous JH, Doupe P. Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial. Ethn Dis 2020;30:217-28. [PMID: 32269464 DOI: 10.18865/ed.30.S1.217] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
289 Ming C, Viassolo V, Probst-Hensch N, Chappuis PO, Dinov ID, Katapodi MC. Letter to the editor: Response to Giardiello D, Antoniou AC, Mariani L, Easton DF, Steyerberg EW. Breast Cancer Res 2020;22:35. [PMID: 32276659 DOI: 10.1186/s13058-020-01274-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
290 Behrend MR, Basáñez MG, Hamley JID, Porco TC, Stolk WA, Walker M, de Vlas SJ; NTD Modelling Consortium. Modelling for policy: The five principles of the Neglected Tropical Diseases Modelling Consortium. PLoS Negl Trop Dis 2020;14:e0008033. [PMID: 32271755 DOI: 10.1371/journal.pntd.0008033] [Cited by in Crossref: 42] [Cited by in F6Publishing: 42] [Article Influence: 14.0] [Reference Citation Analysis]
291 Klimuntowski M, Alam MM, Singh G, Howlader MMR. Electrochemical Sensing of Cannabinoids in Biofluids: A Noninvasive Tool for Drug Detection. ACS Sens 2020;5:620-36. [PMID: 32102542 DOI: 10.1021/acssensors.9b02390] [Cited by in Crossref: 25] [Cited by in F6Publishing: 28] [Article Influence: 8.3] [Reference Citation Analysis]
292 Mongan J, Moy L, Kahn CE Jr. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers.Radiol Artif Intell. 2020;2:e200029. [PMID: 33937821 DOI: 10.1148/ryai.2020200029] [Cited by in Crossref: 194] [Cited by in F6Publishing: 215] [Article Influence: 64.7] [Reference Citation Analysis]
293 Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. NPJ Digit Med 2020;3:43. [PMID: 32219184 DOI: 10.1038/s41746-020-0233-7] [Cited by in Crossref: 79] [Cited by in F6Publishing: 103] [Article Influence: 26.3] [Reference Citation Analysis]
294 Yusuf M, Atal I, Li J, Smith P, Ravaud P, Fergie M, Callaghan M, Selfe J. Reporting quality of studies using machine learning models for medical diagnosis: a systematic review. BMJ Open 2020;10:e034568. [PMID: 32205374 DOI: 10.1136/bmjopen-2019-034568] [Cited by in Crossref: 35] [Cited by in F6Publishing: 35] [Article Influence: 11.7] [Reference Citation Analysis]
295 Tosado J, Zdilar L, Elhalawani H, Elgohari B, Vock DM, Marai GE, Fuller C, Mohamed ASR, Canahuate G. Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction. Sci Rep 2020;10:3811. [PMID: 32123193 DOI: 10.1038/s41598-020-60140-0] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
296 Pickhardt PJ, Graffy PM, Zea R, Lee SJ, Liu J, Sandfort V, Summers RM. Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. Lancet Digit Health 2020;2:e192-200. [PMID: 32864598 DOI: 10.1016/S2589-7500(20)30025-X] [Cited by in Crossref: 69] [Cited by in F6Publishing: 47] [Article Influence: 23.0] [Reference Citation Analysis]
297 Ortiz A, Costa C, Silva R, Biazevic M, Michel-crosato E. Sex estimation: Anatomical references on panoramic radiographs using Machine Learning. Forensic Imaging 2020;20:200356. [DOI: 10.1016/j.fri.2020.200356] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
298 Hendrickx LAM, Sobol GL, Langerhuizen DWG, Bulstra AEJ, Hreha J, Sprague S, Sirkin MS, Ring D, Kerkhoffs GMMJ, Jaarsma RL, Doornberg JN; Machine Learning Consortium. A Machine Learning Algorithm to Predict the Probability of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures to Guide "Malleolus First" Fixation. J Orthop Trauma 2020;34:131-8. [PMID: 32108120 DOI: 10.1097/BOT.0000000000001663] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 6.3] [Reference Citation Analysis]
299 Montagnon E, Cerny M, Cadrin-Chênevert A, Hamilton V, Derennes T, Ilinca A, Vandenbroucke-Menu F, Turcotte S, Kadoury S, Tang A. Deep learning workflow in radiology: a primer. Insights Imaging 2020;11:22. [PMID: 32040647 DOI: 10.1186/s13244-019-0832-5] [Cited by in Crossref: 61] [Cited by in F6Publishing: 65] [Article Influence: 20.3] [Reference Citation Analysis]
300 Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, Askautrud HA, Pradhan M, Nesheim JA, Albregtsen F, Farstad IN, Domingo E, Church DN, Nesbakken A, Shepherd NA, Tomlinson I, Kerr R, Novelli M, Kerr DJ, Danielsen HE. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet 2020;395:350-60. [PMID: 32007170 DOI: 10.1016/S0140-6736(19)32998-8] [Cited by in Crossref: 183] [Cited by in F6Publishing: 117] [Article Influence: 61.0] [Reference Citation Analysis]
301 López Seguí F, Ander Egg Aguilar R, de Maeztu G, García-Altés A, García Cuyàs F, Walsh S, Sagarra Castro M, Vidal-Alaball J. Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning. Int J Environ Res Public Health 2020;17:E1093. [PMID: 32050435 DOI: 10.3390/ijerph17031093] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 3.0] [Reference Citation Analysis]
302 Young CM, Luo W, Gastin PB, Dwyer DB. Understanding the relative contribution of technical and tactical performance to match outcome in Australian Football. J Sports Sci 2020;38:676-81. [PMID: 32028853 DOI: 10.1080/02640414.2020.1724044] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
303 Luo Y, Tang Z, Hu X, Lu S, Miao B, Hong S, Bai H, Sun C, Qiu J, Liang H, Na N. Machine learning for the prediction of severe pneumonia during posttransplant hospitalization in recipients of a deceased-donor kidney transplant. Ann Transl Med. 2020;8:82. [PMID: 32175375 DOI: 10.21037/atm.2020.01.09] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 4.7] [Reference Citation Analysis]
304 Ordovas KG, Seo Y. Artificial Intelligence Pipeline for Risk Prediction in Cardiovascular Imaging. Circ: Cardiovascular Imaging 2020;13. [DOI: 10.1161/circimaging.120.010427] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
305 Neubauer NA, Liu L. Development and validation of a conceptual model and strategy adoption guidelines for persons with dementia at risk of getting lost. Dementia (London) 2021;20:534-55. [PMID: 31969006 DOI: 10.1177/1471301219898350] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
306 de Keijzer IN, Vos JJ, Scheeren TWL. Hypotension Prediction Index: from proof-of-concept to proof-of-feasibility. J Clin Monit Comput 2020;34:1135-8. [DOI: 10.1007/s10877-020-00465-3] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
307 Ershoff BD, Lee CK, Wray CL, Agopian VG, Urban G, Baldi P, Cannesson M. Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data. Transplant Proc 2020;52:246-58. [PMID: 31926745 DOI: 10.1016/j.transproceed.2019.10.019] [Cited by in Crossref: 17] [Cited by in F6Publishing: 10] [Article Influence: 5.7] [Reference Citation Analysis]
308 Tran HP, Tran LN, Dang HT, Vu TD, Trinh DT, Pham BT, Sang VNT. A SWOT Analysis of Human- and Machine Learning- Based Embryo Assessment. IEEE Access 2020;8:227466-227481. [DOI: 10.1109/access.2020.3045772] [Reference Citation Analysis]
309 Khan O, Badhiwala JH, Wilson JRF, Jiang F, Martin AR, Fehlings MG. Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions. Neurospine 2019;16:678-85. [PMID: 31905456 DOI: 10.14245/ns.1938390.195] [Cited by in Crossref: 19] [Cited by in F6Publishing: 19] [Article Influence: 4.8] [Reference Citation Analysis]
310 Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers. Korean J Radiol 2019;20:405-10. [PMID: 30799571 DOI: 10.3348/kjr.2019.0025] [Cited by in Crossref: 196] [Cited by in F6Publishing: 205] [Article Influence: 49.0] [Reference Citation Analysis]
311 Zhang X, Bellolio MF, Medrano-Gracia P, Werys K, Yang S, Mahajan P. Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department. BMC Med Inform Decis Mak 2019;19:287. [PMID: 31888609 DOI: 10.1186/s12911-019-1006-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 1.5] [Reference Citation Analysis]
312 Calanna P, Lauriola M, Saggino A, Tommasi M, Furlan S. Using a supervised machine learning algorithm for detecting faking good in a personality self‐report. Int J Select Assess 2020;28:176-85. [DOI: 10.1111/ijsa.12279] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
313 Moon SJ, Hwang J, Kana R, Torous J, Kim JW. Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies. JMIR Ment Health 2019;6:e14108. [PMID: 31562756 DOI: 10.2196/14108] [Cited by in Crossref: 26] [Cited by in F6Publishing: 26] [Article Influence: 6.5] [Reference Citation Analysis]
314 Smith M, Dietrich BJ, Bai EW, Bockholt HJ. Vocal pattern detection of depression among older adults. Int J Ment Health Nurs 2020;29:440-9. [PMID: 31811697 DOI: 10.1111/inm.12678] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
315 Karhade AV, Shah AA, Bono CM, Ferrone ML, Nelson SB, Schoenfeld AJ, Harris MB, Schwab JH. Development of machine learning algorithms for prediction of mortality in spinal epidural abscess. Spine J 2019;19:1950-9. [PMID: 31255788 DOI: 10.1016/j.spinee.2019.06.024] [Cited by in Crossref: 28] [Cited by in F6Publishing: 29] [Article Influence: 7.0] [Reference Citation Analysis]
316 Sullivan SS, Hewner S, Chandola V, Westra BL. Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data. Nurs Res 2019;68:156-66. [PMID: 30531348 DOI: 10.1097/NNR.0000000000000328] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
317 Sheyn D, Ju M, Zhang S, Anyaeche C, Hijaz A, Mangel J, Mahajan S, Conroy B, El-nashar S, Ray S. Development and Validation of a Machine Learning Algorithm for Predicting Response to Anticholinergic Medications for Overactive Bladder Syndrome. Obstetrics & Gynecology 2019;134:946-57. [DOI: 10.1097/aog.0000000000003517] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
318 Karhade AV, Ogink PT, Thio QCBS, Cha TD, Gormley WB, Hershman SH, Smith TR, Mao J, Schoenfeld AJ, Bono CM, Schwab JH. Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation. Spine J 2019;19:1764-71. [PMID: 31185292 DOI: 10.1016/j.spinee.2019.06.002] [Cited by in Crossref: 54] [Cited by in F6Publishing: 57] [Article Influence: 13.5] [Reference Citation Analysis]
319 Thomsen K, Iversen L, Titlestad TL, Winther O. Systematic review of machine learning for diagnosis and prognosis in dermatology. J Dermatolog Treat 2020;31:496-510. [PMID: 31625775 DOI: 10.1080/09546634.2019.1682500] [Cited by in Crossref: 32] [Cited by in F6Publishing: 21] [Article Influence: 8.0] [Reference Citation Analysis]
320 Wei W, Wang K, Liu Z, Tian K, Wang L, Du J, Ma J, Wang S, Li L, Zhao R, Cui L, Wu Z, Tian J. Radiomic signature: A novel magnetic resonance imaging-based prognostic biomarker in patients with skull base chordoma. Radiother Oncol 2019;141:239-46. [PMID: 31668985 DOI: 10.1016/j.radonc.2019.10.002] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 2.8] [Reference Citation Analysis]
321 Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone. JMIR Mhealth Uhealth 2019;7:e14149. [PMID: 31621642 DOI: 10.2196/14149] [Cited by in Crossref: 47] [Cited by in F6Publishing: 48] [Article Influence: 11.8] [Reference Citation Analysis]
322 Spence J, Mazer CD. The Future Directions of Research in Cardiac Anesthesiology. Anesthesiol Clin 2019;37:801-13. [PMID: 31677692 DOI: 10.1016/j.anclin.2019.08.008] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
323 Sufriyana H, Husnayain A, Chen Y, Kuo C, Singh O, Yeh T, Wu Y, Su EC. Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis (Preprint).. [DOI: 10.2196/preprints.16503] [Reference Citation Analysis]
324 Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam JR, Schmid MK, Balaskas K, Topol EJ, Bachmann LM, Keane PA, Denniston AK. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health 2019;1:e271-97. [PMID: 33323251 DOI: 10.1016/S2589-7500(19)30123-2] [Cited by in Crossref: 622] [Cited by in F6Publishing: 619] [Article Influence: 155.5] [Reference Citation Analysis]
325 Karhade AV, Schwab JH, Bedair HS. Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty. J Arthroplasty 2019;34:2272-2277.e1. [PMID: 31327647 DOI: 10.1016/j.arth.2019.06.013] [Cited by in Crossref: 64] [Cited by in F6Publishing: 63] [Article Influence: 16.0] [Reference Citation Analysis]
326 Fritz BA, Cui Z, Zhang M, He Y, Chen Y, Kronzer A, Ben Abdallah A, King CR, Avidan MS. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth 2019;123:688-95. [PMID: 31558311 DOI: 10.1016/j.bja.2019.07.025] [Cited by in Crossref: 42] [Cited by in F6Publishing: 46] [Article Influence: 10.5] [Reference Citation Analysis]
327 Farran B, AlWotayan R, Alkandari H, Al-Abdulrazzaq D, Channanath A, Thanaraj TA. Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait. Front Endocrinol (Lausanne) 2019;10:624. [PMID: 31572303 DOI: 10.3389/fendo.2019.00624] [Cited by in Crossref: 20] [Cited by in F6Publishing: 22] [Article Influence: 5.0] [Reference Citation Analysis]
328 Tandon N, Tandon R. Machine learning in psychiatry- standards and guidelines. Asian J Psychiatr 2019;44:A1-4. [PMID: 31530438 DOI: 10.1016/j.ajp.2019.09.009] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.0] [Reference Citation Analysis]
329 Tandon N, Tandon R. Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype. Schizophr Res 2019;214:70-5. [PMID: 31500998 DOI: 10.1016/j.schres.2019.08.032] [Cited by in Crossref: 18] [Cited by in F6Publishing: 18] [Article Influence: 4.5] [Reference Citation Analysis]
330 Young C, Luo W, Gastin P, Tran J, Dwyer D. Modelling Match Outcome in Australian Football: Improved accuracy with large databases. International Journal of Computer Science in Sport 2019;18:80-92. [DOI: 10.2478/ijcss-2019-0005] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 1.3] [Reference Citation Analysis]
331 Flechet M, Falini S, Bonetti C, Güiza F, Schetz M, Van den Berghe G, Meyfroidt G. Machine learning versus physicians' prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor. Crit Care 2019;23:282. [PMID: 31420056 DOI: 10.1186/s13054-019-2563-x] [Cited by in Crossref: 39] [Cited by in F6Publishing: 42] [Article Influence: 9.8] [Reference Citation Analysis]
332 Lee CK, Hofer I, Gabel E, Baldi P, Cannesson M. Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality. Anesthesiology 2018;129:649-62. [PMID: 29664888 DOI: 10.1097/ALN.0000000000002186] [Cited by in Crossref: 91] [Cited by in F6Publishing: 96] [Article Influence: 22.8] [Reference Citation Analysis]
333 Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, Rinehart J, Cannesson M. Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology. 2018;129:663-674. [PMID: 29894315 DOI: 10.1097/aln.0000000000002300] [Cited by in Crossref: 204] [Cited by in F6Publishing: 210] [Article Influence: 51.0] [Reference Citation Analysis]
334 Mathis MR, Kheterpal S, Najarian K. Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know: More than Black Magic for the Art of the Dark. Anesthesiology 2018;129:619-22. [PMID: 30080689 DOI: 10.1097/ALN.0000000000002384] [Cited by in Crossref: 29] [Cited by in F6Publishing: 30] [Article Influence: 7.3] [Reference Citation Analysis]
335 Danielsen AA, Fenger MHJ, Østergaard SD, Nielbo KL, Mors O. Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data. Acta Psychiatr Scand 2019;140:147-57. [PMID: 31209866 DOI: 10.1111/acps.13061] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
336 F.i. Osman A. Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine. Artificial Intelligence - Applications in Medicine and Biology 2019. [DOI: 10.5772/intechopen.84629] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
337 Weenk M, Bredie SJ, Koeneman M, Hesselink G, van Goor H, van de Belt TH. Continuous Monitoring of Vital Signs in the General Ward Using Wearable Devices: Randomized Controlled Trial (Preprint).. [DOI: 10.2196/preprints.15471] [Reference Citation Analysis]
338 Sufriyana H, Wu Y, Su EC. Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort (Preprint).. [DOI: 10.2196/preprints.15411] [Reference Citation Analysis]
339 Doupe P, Faghmous J, Basu S. Machine Learning for Health Services Researchers. Value in Health 2019;22:808-15. [DOI: 10.1016/j.jval.2019.02.012] [Cited by in Crossref: 81] [Cited by in F6Publishing: 50] [Article Influence: 20.3] [Reference Citation Analysis]
340 Dihge L, Ohlsson M, Edén P, Bendahl PO, Rydén L. Artificial neural network models to predict nodal status in clinically node-negative breast cancer. BMC Cancer 2019;19:610. [PMID: 31226956 DOI: 10.1186/s12885-019-5827-6] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 3.3] [Reference Citation Analysis]
341 Park SH, Kim Y, Lee JY, Yoo S, Kim CJ. Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review. Sci Ed 2019;6:91-8. [DOI: 10.6087/kcse.164] [Cited by in Crossref: 11] [Cited by in F6Publishing: 3] [Article Influence: 2.8] [Reference Citation Analysis]
342 Zhao RF, Zhang WY, Zhou L, Chen Y. Building a predictive model for successful vaginal delivery in nulliparas with term cephalic singleton pregnancies using decision tree analysis. J Obstet Gynaecol Res 2019;45:1536-44. [PMID: 31161703 DOI: 10.1111/jog.14011] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
343 Saugel B, Kouz K, Hoppe P, Maheshwari K, Scheeren TW. Predicting hypotension in perioperative and intensive care medicine. Best Practice & Research Clinical Anaesthesiology 2019;33:189-97. [DOI: 10.1016/j.bpa.2019.04.001] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 3.5] [Reference Citation Analysis]
344 Karhade AV, Ogink PT, Thio QCBS, Broekman MLD, Cha TD, Hershman SH, Mao J, Peul WC, Schoenfeld AJ, Bono CM, Schwab JH. Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion. Spine J 2019;19:976-83. [PMID: 30710731 DOI: 10.1016/j.spinee.2019.01.009] [Cited by in Crossref: 80] [Cited by in F6Publishing: 80] [Article Influence: 20.0] [Reference Citation Analysis]
345 Faes L, Wagner SK, Fu DJ, Liu X, Korot E, Ledsam JR, Back T, Chopra R, Pontikos N, Kern C, Moraes G, Schmid MK, Sim D, Balaskas K, Bachmann LM, Denniston AK, Keane PA. Feasibility of Automated Deep Learning Design for Medical Image Classification by Healthcare Professionals with Limited Coding Experience.. [DOI: 10.1101/650366] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
346 Molina-García D, Vera-Ramírez L, Pérez-Beteta J, Arana E, Pérez-García VM. Prognostic models based on imaging findings in glioblastoma: Human versus Machine. Sci Rep 2019;9:5982. [PMID: 30979965 DOI: 10.1038/s41598-019-42326-3] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 2.8] [Reference Citation Analysis]
347 Triantafyllidis AK, Tsanas A. Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature. J Med Internet Res 2019;21:e12286. [PMID: 30950797 DOI: 10.2196/12286] [Cited by in Crossref: 97] [Cited by in F6Publishing: 105] [Article Influence: 24.3] [Reference Citation Analysis]
348 Young CM, Luo W, Gastin P, Tran J, Dwyer DB. The relationship between match performance indicators and outcome in Australian Football. Journal of Science and Medicine in Sport 2019;22:467-71. [DOI: 10.1016/j.jsams.2018.09.235] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 3.8] [Reference Citation Analysis]
349 Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone (Preprint).. [DOI: 10.2196/preprints.14149] [Reference Citation Analysis]
350 Moon SJ, Hwang JS, Kana R, Torous J, Kim JW. Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint).. [DOI: 10.2196/preprints.14108] [Reference Citation Analysis]
351 Schultebraucks K, Galatzer‐levy IR. Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances. JOURNAL OF TRAUMATIC STRESS 2019;32:215-25. [DOI: 10.1002/jts.22384] [Cited by in Crossref: 38] [Cited by in F6Publishing: 43] [Article Influence: 9.5] [Reference Citation Analysis]
352 Karhade AV, Thio QCBS, Ogink PT, Bono CM, Ferrone ML, Oh KS, Saylor PJ, Schoenfeld AJ, Shin JH, Harris MB, Schwab JH. Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation. Neurosurgery 2019;85:E671-81. [DOI: 10.1093/neuros/nyz070] [Cited by in Crossref: 87] [Cited by in F6Publishing: 94] [Article Influence: 21.8] [Reference Citation Analysis]
353 Weenk M, Koeneman M, van de Belt TH, Engelen LJ, van Goor H, Bredie SJ. Wireless and continuous monitoring of vital signs in patients at the general ward. Resuscitation 2019;136:47-53. [DOI: 10.1016/j.resuscitation.2019.01.017] [Cited by in Crossref: 64] [Cited by in F6Publishing: 67] [Article Influence: 16.0] [Reference Citation Analysis]
354 Carson NJ, Mullin B, Sanchez MJ, Lu F, Yang K, Menezes M, Cook BL. Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records. PLoS One 2019;14:e0211116. [PMID: 30779800 DOI: 10.1371/journal.pone.0211116] [Cited by in Crossref: 32] [Cited by in F6Publishing: 33] [Article Influence: 8.0] [Reference Citation Analysis]
355 Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12-22. [PMID: 30763612 DOI: 10.1016/j.jclinepi.2019.02.004] [Cited by in Crossref: 581] [Cited by in F6Publishing: 622] [Article Influence: 145.3] [Reference Citation Analysis]
356 Parisi L, Ravichandran N, Manaog ML. A novel hybrid algorithm for aiding prediction of prognosis in patients with hepatitis. Neural Comput & Applic 2020;32:3839-52. [DOI: 10.1007/s00521-019-04050-x] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 2.3] [Reference Citation Analysis]
357 Allen B, Gish R, Dreyer K. The Role of an Artificial Intelligence Ecosystem in Radiology. In: Ranschaert ER, Morozov S, Algra PR, editors. Artificial Intelligence in Medical Imaging. Cham: Springer International Publishing; 2019. pp. 291-327. [DOI: 10.1007/978-3-319-94878-2_19] [Cited by in Crossref: 2] [Article Influence: 0.5] [Reference Citation Analysis]
358 Curchoe CL, Bormann CL. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet 2019;36:591-600. [PMID: 30690654 DOI: 10.1007/s10815-019-01408-x] [Cited by in Crossref: 61] [Cited by in F6Publishing: 67] [Article Influence: 15.3] [Reference Citation Analysis]
359 Panchagnula U, Shanmugam M, Rao BM. Digital future in perioperative medicine: Are we there yet? J Anaesthesiol Clin Pharmacol 2019;35:292-4. [PMID: 31543574 DOI: 10.4103/joacp.JOACP_228_19] [Reference Citation Analysis]
360 Dankers FJWM, Traverso A, Wee L, van Kuijk SMJ. Prediction Modeling Methodology. Fundamentals of Clinical Data Science 2019. [DOI: 10.1007/978-3-319-99713-1_8] [Cited by in Crossref: 30] [Cited by in F6Publishing: 28] [Article Influence: 7.5] [Reference Citation Analysis]
361 Shirole U, Joshi M, Bagul P. Cardiac, diabetic and normal subjects classification using decision tree and result confirmation through orthostatic stress index. Informatics in Medicine Unlocked 2019;17:100252. [DOI: 10.1016/j.imu.2019.100252] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
362 Salagre E, Dodd S, Aedo A, Rosa A, Amoretti S, Pinzon J, Reinares M, Berk M, Kapczinski FP, Vieta E, Grande I. Toward Precision Psychiatry in Bipolar Disorder: Staging 2.0. Front Psychiatry 2018;9:641. [PMID: 30555363 DOI: 10.3389/fpsyt.2018.00641] [Cited by in Crossref: 51] [Cited by in F6Publishing: 67] [Article Influence: 10.2] [Reference Citation Analysis]
363 Op den Buijs J, Simons M, Golas S, Fischer N, Felsted J, Schertzer L, Agboola S, Kvedar J, Jethwani K. Predictive Modeling of 30-Day Emergency Hospital Transport of Patients Using a Personal Emergency Response System: Prognostic Retrospective Study. JMIR Med Inform 2018;6:e49. [PMID: 30482741 DOI: 10.2196/medinform.9907] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.0] [Reference Citation Analysis]
364 Rahimian F, Salimi-Khorshidi G, Payberah AH, Tran J, Ayala Solares R, Raimondi F, Nazarzadeh M, Canoy D, Rahimi K. Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records. PLoS Med 2018;15:e1002695. [PMID: 30458006 DOI: 10.1371/journal.pmed.1002695] [Cited by in Crossref: 63] [Cited by in F6Publishing: 66] [Article Influence: 12.6] [Reference Citation Analysis]
365 Karhade AV, Ogink P, Thio Q, Broekman M, Cha T, Gormley WB, Hershman S, Peul WC, Bono CM, Schwab JH. Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders. Neurosurgical Focus 2018;45:E6. [DOI: 10.3171/2018.8.focus18340] [Cited by in Crossref: 56] [Cited by in F6Publishing: 57] [Article Influence: 11.2] [Reference Citation Analysis]
366 Verrusio W, Renzi A, Dellepiane U, Renzi S, Zaccone M, Gueli N, Cacciafesta M. A new tool for the evaluation of the rehabilitation outcomes in older persons: a machine learning model to predict functional status 1 year ahead. Eur Geriatr Med 2018;9:651-7. [PMID: 34654230 DOI: 10.1007/s41999-018-0098-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
367 Kendale S, Kulkarni P, Rosenberg AD, Wang J. Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension. Anesthesiology 2018;129:675-88. [DOI: 10.1097/aln.0000000000002374] [Cited by in Crossref: 85] [Cited by in F6Publishing: 90] [Article Influence: 17.0] [Reference Citation Analysis]
368 Kakarmath S, Golas S, Felsted J, Kvedar J, Jethwani K, Agboola S. Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study. JMIR Res Protoc 2018;7:e176. [PMID: 30181113 DOI: 10.2196/resprot.9466] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 1.6] [Reference Citation Analysis]
369 El Naqa I, Ruan D, Valdes G, Dekker A, McNutt T, Ge Y, Wu QJ, Oh JH, Thor M, Smith W, Rao A, Fuller C, Xiao Y, Manion F, Schipper M, Mayo C, Moran JM, Ten Haken R. Machine learning and modeling: Data, validation, communication challenges. Med Phys 2018;45:e834-40. [PMID: 30144098 DOI: 10.1002/mp.12811] [Cited by in Crossref: 49] [Cited by in F6Publishing: 50] [Article Influence: 9.8] [Reference Citation Analysis]
370 Fransquet PD, Ryan J. Micro RNA as a potential blood-based epigenetic biomarker for Alzheimer's disease. Clin Biochem. 2018;58:5-14. [PMID: 29885309 DOI: 10.1016/j.clinbiochem.2018.05.020] [Cited by in Crossref: 45] [Cited by in F6Publishing: 50] [Article Influence: 9.0] [Reference Citation Analysis]
371 Jethanandani A, Lin TA, Volpe S, Elhalawani H, Mohamed ASR, Yang P, Fuller CD. Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review. Front Oncol 2018;8:131. [PMID: 29868465 DOI: 10.3389/fonc.2018.00131] [Cited by in Crossref: 55] [Cited by in F6Publishing: 61] [Article Influence: 11.0] [Reference Citation Analysis]
372 Chi TY, Zhu HM, Zhang M. Risk factors associated with nonsteroidal anti-inflammatory drugs (NSAIDs)-induced gastrointestinal bleeding resulting on people over 60 years old in Beijing. Medicine (Baltimore) 2018;97:e0665. [PMID: 29718891 DOI: 10.1097/MD.0000000000010665] [Cited by in Crossref: 18] [Cited by in F6Publishing: 18] [Article Influence: 3.6] [Reference Citation Analysis]
373 Zarinabad N, Meeus EM, Manias K, Foster K, Peet A. Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis. JMIR Med Inform 2018;6:e30. [PMID: 29720361 DOI: 10.2196/medinform.9171] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 0.2] [Reference Citation Analysis]
374 Park SH, Kressel HY. Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do. J Korean Med Sci 2018;33:e152. [PMID: 29805337 DOI: 10.3346/jkms.2018.33.e152] [Cited by in Crossref: 27] [Cited by in F6Publishing: 32] [Article Influence: 5.4] [Reference Citation Analysis]
375 Koprowski R, Foster KR. Machine learning and medicine: book review and commentary. Biomed Eng Online 2018;17:17. [PMID: 29391026 DOI: 10.1186/s12938-018-0449-9] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 2.4] [Reference Citation Analysis]
376 op den Buijs J, Simons M, Golas S, Fischer N, Felsted J, Schertzer L, Agboola S, Kvedar J, Jethwani K. Predictive Modeling of 30-Day Emergency Hospital Transport of Patients Using a Personal Emergency Response System: Prognostic Retrospective Study (Preprint).. [DOI: 10.2196/preprints.9907] [Reference Citation Analysis]
377 Park SH, Do K, Choi J, Sim JS, Yang DM, Eo H, Woo H, Lee JM, Jung SE, Oh JH. Principles for evaluating the clinical implementation of novel digital healthcare devices. J Korean Med Assoc 2018;61:765. [DOI: 10.5124/jkma.2018.61.12.765] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 3.2] [Reference Citation Analysis]
378 Thomas Homescu A. Leveraging Big Data for Personalized Treatment of Anxiety and Depression: Review and Possible Future Directions. SSRN Journal. [DOI: 10.2139/ssrn.3187926] [Reference Citation Analysis]
379 Kakarmath S, Golas S, Felsted J, Kvedar J, Jethwani K, Agboola S. Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study (Preprint).. [DOI: 10.2196/preprints.9466] [Reference Citation Analysis]
380 Zarinabad N, Meeus EM, Manias K, Foster K, Peet A. Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis (Preprint).. [DOI: 10.2196/preprints.9171] [Reference Citation Analysis]
381 Weenk M, van Goor H, Frietman B, Engelen LJ, van Laarhoven CJ, Smit J, Bredie SJ, van de Belt TH. Continuous Monitoring of Vital Signs Using Wearable Devices on the General Ward: Pilot Study. JMIR Mhealth Uhealth 2017;5:e91. [PMID: 28679490 DOI: 10.2196/mhealth.7208] [Cited by in Crossref: 113] [Cited by in F6Publishing: 115] [Article Influence: 18.8] [Reference Citation Analysis]