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For: Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19:64. [PMID: 30890124 DOI: 10.1186/s12874-019-0681-4] [Cited by in Crossref: 135] [Cited by in F6Publishing: 95] [Article Influence: 45.0] [Reference Citation Analysis]
Number Citing Articles
1 Ballotin VR, Bigarella LG, Soldera J, Soldera J. Deep learning applied to the imaging diagnosis of hepatocellular carcinoma. Artif Intell Gastrointest Endosc 2021; 2(4): 127-135 [DOI: 10.37126/aige.v2.i4.127] [Reference Citation Analysis]
2 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] [Reference Citation Analysis]
3 Perpetuo L, Klein J, Ferreira R, Guedes S, Amado F, Leite-Moreira A, Silva AMS, Thongboonkerd V, Vitorino R. How can artificial intelligence be used for peptidomics? Expert Rev Proteomics 2021. [PMID: 34343059 DOI: 10.1080/14789450.2021.1962303] [Reference Citation Analysis]
4 Reed RA, Morgan AS, Zeitlin J, Jarreau PH, Torchin H, Pierrat V, Ancel PY, Khoshnood B. Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2). Front Pediatr 2020;8:585868. [PMID: 33614539 DOI: 10.3389/fped.2020.585868] [Reference Citation Analysis]
5 Benedetto U, Dimagli A, Sinha S, Cocomello L, Gibbison B, Caputo M, Gaunt T, Lyon M, Holmes C, Angelini GD. Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis. J Thorac Cardiovasc Surg 2020:S0022-5223(20)32357-6. [PMID: 32900480 DOI: 10.1016/j.jtcvs.2020.07.105] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
6 Shou L, Huang WW, Barszczyk A, Wu SJ, Han H, Waese-Perlman A, Chen L, Wei J, Luo H, Lee K. Blood Biomarkers Predict Cardiac Workload Using Machine Learning. Biomed Res Int 2021;2021:6172815. [PMID: 34159195 DOI: 10.1155/2021/6172815] [Reference Citation Analysis]
7 Okada Y, Matsuyama T, Morita S, Ehara N, Miyamae N, Jo T, Sumida Y, Okada N, Watanabe M, Nozawa M, Tsuruoka A, Fujimoto Y, Okumura Y, Kitamura T, Iiduka R, Ohtsuru S. Machine learning-based prediction models for accidental hypothermia patients. J Intensive Care 2021;9:6. [PMID: 33422146 DOI: 10.1186/s40560-021-00525-z] [Reference Citation Analysis]
8 Panchavati S, Lam C, Zelin NS, Pellegrini E, Barnes G, Hoffman J, Garikipati A, Calvert J, Mao Q, Das R. Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification. Healthc Technol Lett 2021;8:139-47. [PMID: 34938570 DOI: 10.1049/htl2.12017] [Reference Citation Analysis]
9 Abdesselam A, Zidoum H, Zadjali F, Hedjam R, Al-Ansari A, Bayoumi R, Al-Yahyaee S, Hassan M, Albarwani S. Estimate of the HOMA-IR Cut-off Value for Identifying Subjects at Risk of Insulin Resistance Using a Machine Learning Approach. Sultan Qaboos Univ Med J 2021;21:604-12. [PMID: 34888081 DOI: 10.18295/squmj.4.2021.030] [Reference Citation Analysis]
10 Cuocolo R, Caruso M, Perillo T, Ugga L, Petretta M. Machine Learning in oncology: A clinical appraisal. Cancer Lett. 2020;481:55-62. [PMID: 32251707 DOI: 10.1016/j.canlet.2020.03.032] [Cited by in Crossref: 31] [Cited by in F6Publishing: 29] [Article Influence: 15.5] [Reference Citation Analysis]
11 Saberi-Karimian M, Khorasanchi Z, Ghazizadeh H, Tayefi M, Saffar S, Ferns GA, Ghayour-Mobarhan M. Potential value and impact of data mining and machine learning in clinical diagnostics. Crit Rev Clin Lab Sci 2021;58:275-96. [PMID: 33739235 DOI: 10.1080/10408363.2020.1857681] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Gupta AK, Ivanova IA, Renaud HJ. How good is artificial intelligence (AI) at solving hairy problems? A review of AI applications in hair restoration and hair disorders. Dermatol Ther 2021;34:e14811. [PMID: 33496058 DOI: 10.1111/dth.14811] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
13 Lalehzarian SP, Gowd AK, Liu JN. Machine learning in orthopaedic surgery. World J Orthop 2021; 12(9): 685-699 [PMID: 34631452 DOI: 10.5312/wjo.v12.i9.685] [Reference Citation Analysis]
14 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: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
15 De Jesus-Rodriguez HJ, Morgan MA, Sagreiya H. Deep Learning in Kidney Ultrasound: Overview, Frontiers, and Challenges. Adv Chronic Kidney Dis 2021;28:262-9. [PMID: 34906311 DOI: 10.1053/j.ackd.2021.07.004] [Reference Citation Analysis]
16 Mlodzinski E, Stone DJ, Celi LA. Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review. Pulm Ther 2020;6:67-77. [PMID: 32048244 DOI: 10.1007/s41030-020-00110-z] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
17 Majnarić LT, Babič F, O'Sullivan S, Holzinger A. AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity. J Clin Med 2021;10:766. [PMID: 33672914 DOI: 10.3390/jcm10040766] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
18 Flores AM, Demsas F, Leeper NJ, Ross EG. Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes. Circ Res 2021;128:1833-50. [PMID: 34110911 DOI: 10.1161/CIRCRESAHA.121.318224] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021;20:154-70. [PMID: 34002503 DOI: 10.1002/wps.20882] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
20 Punjani N, Kang C, Lee RK, Goldstein M, Li PS. Technological Advancements in Male Infertility Microsurgery. J Clin Med 2021;10:4259. [PMID: 34575370 DOI: 10.3390/jcm10184259] [Reference Citation Analysis]
21 Patel AV, White CA, Schwartz JT, Pitaro NL, Shah KC, Singh S, Arvind V, Kim JS, Cho SK. Emerging Technologies in the Treatment of Adult Spinal Deformity. Neurospine 2021;18:417-27. [PMID: 34610669 DOI: 10.14245/ns.2142412.206] [Reference Citation Analysis]
22 Tyagi A, Tiwari P, Bhardwaj P, Chawla H. Prognosis of sexual dimorphism with unfused hyoid bone: Artificial intelligence informed decision making with discriminant analysis. Sci Justice 2021;61:789-96. [PMID: 34802653 DOI: 10.1016/j.scijus.2021.10.002] [Reference Citation Analysis]
23 Nagaraj S, Duong TQ. Deep Learning and Risk Score Classification of Mild Cognitive Impairment and Alzheimer's Disease. J Alzheimers Dis 2021;80:1079-90. [PMID: 33646166 DOI: 10.3233/JAD-201438] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Kerner J, Dogan A, von Recum H. Machine learning and big data provide crucial insight for future biomaterials discovery and research. Acta Biomater 2021;130:54-65. [PMID: 34087445 DOI: 10.1016/j.actbio.2021.05.053] [Reference Citation Analysis]
25 Wang L, Heywood A, Stocks J, Bae J, Ma D, Popuri K, Toga AW, Kantarci K, Younes L, Mackenzie IR, Zhang F, Beg MF, Rosen H; Alzheimer’s Disease Neuroimaging Initiative. Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis. J Psychiatr Brain Sci 2019;4:e190017. [PMID: 31754634 DOI: 10.20900/jpbs.20190017] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
26 Zhou M, Shi Z, Li X, Cai L, Ding Y, Si Y, Deng H, Fu W. Prediction of Distal Aortic Enlargement after Proximal Repair of Aortic Dissection Using Machine Learning. Ann Vasc Surg 2021;75:332-40. [PMID: 33823266 DOI: 10.1016/j.avsg.2021.02.039] [Reference Citation Analysis]
27 Schwartz AR, Cohen-Zion M, Pham LV, Gal A, Sowho M, Sgambati FP, Klopfer T, Guzman MA, Hawks EM, Etzioni T, Glasner L, Druckman E, Pillar G. Brief digital sleep questionnaire powered by machine learning prediction models identifies common sleep disorders. Sleep Med 2020;71:66-76. [PMID: 32502852 DOI: 10.1016/j.sleep.2020.03.005] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
28 Hanko M, Grendár M, Snopko P, Opšenák R, Šutovský J, Benčo M, Soršák J, Zeleňák K, Kolarovszki B. Random Forest-Based Prediction of Outcome and Mortality in Patients with Traumatic Brain Injury Undergoing Primary Decompressive Craniectomy. World Neurosurg 2021;148:e450-8. [PMID: 33444843 DOI: 10.1016/j.wneu.2021.01.002] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Khoshmanesh F, Thurgood P, Pirogova E, Nahavandi S, Baratchi S. Wearable sensors: At the frontier of personalised health monitoring, smart prosthetics and assistive technologies. Biosens Bioelectron 2021;176:112946. [PMID: 33412429 DOI: 10.1016/j.bios.2020.112946] [Cited by in Crossref: 13] [Cited by in F6Publishing: 6] [Article Influence: 6.5] [Reference Citation Analysis]
30 Hasimbegovic E, Papp L, Grahovac M, Krajnc D, Poschner T, Hasan W, Andreas M, Gross C, Strouhal A, Delle-Karth G, Grabenwöger M, Adlbrecht C, Mach M. A Sneak-Peek into the Physician's Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis. J Pers Med 2021;11:1062. [PMID: 34834414 DOI: 10.3390/jpm11111062] [Reference Citation Analysis]
31 Lammers D, Marenco C, Morte K, Conner J, Williams J, Bax T, Martin M, Eckert M, Bingham J. Machine Learning for Military Trauma: Novel Massive Transfusion Predictive Models in Combat Zones. J Surg Res 2021;270:369-75. [PMID: 34736129 DOI: 10.1016/j.jss.2021.09.017] [Reference Citation Analysis]
32 Giri PC, Chowdhury AM, Bedoya A, Chen H, Lee HS, Lee P, Henriquez C, MacIntyre NR, Huang YT. Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going? Front Physiol 2021;12:678540. [PMID: 34248665 DOI: 10.3389/fphys.2021.678540] [Reference Citation Analysis]
33 Nakamori Y, Park EJ, Shimaoka M. Immune Deregulation in Sepsis and Septic Shock: Reversing Immune Paralysis by Targeting PD-1/PD-L1 Pathway. Front Immunol 2020;11:624279. [PMID: 33679715 DOI: 10.3389/fimmu.2020.624279] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
34 Golatta M, Pfob A, Büsch C, Bruckner T, Alwafai Z, Balleyguier C, Clevert DA, Duda V, Goncalo M, Gruber I, Hahn M, Kapetas P, Ohlinger R, Rutten M, Tozaki M, Wojcinski S, Rauch G, Heil J, Barr RG. The Potential of Shear Wave Elastography to Reduce Unnecessary Biopsies in Breast Cancer Diagnosis: An International, Diagnostic, Multicenter Trial. Ultraschall Med 2021. [PMID: 34425600 DOI: 10.1055/a-1543-6156] [Reference Citation Analysis]
35 Kwong GA, Ghosh S, Gamboa L, Patriotis C, Srivastava S, Bhatia SN. Synthetic biomarkers: a twenty-first century path to early cancer detection. Nat Rev Cancer 2021;21:655-68. [PMID: 34489588 DOI: 10.1038/s41568-021-00389-3] [Reference Citation Analysis]
36 Okere AN, Sanogo V, Alqhtani H, Diaby V. Identification of risk factors of 30-day readmission and 180-day in-hospital mortality, and its corresponding relative importance in patients with Ischemic heart disease: a machine learning approach. Expert Rev Pharmacoecon Outcomes Res 2021;21:1043-8. [PMID: 33131344 DOI: 10.1080/14737167.2021.1842200] [Reference Citation Analysis]
37 Russak AJ, Chaudhry F, De Freitas JK, Baron G, Chaudhry FF, Bienstock S, Paranjpe I, Vaid A, Ali M, Zhao S, Somani S, Richter F, Bawa T, Levy PD, Miotto R, Nadkarni GN, Johnson KW, Glicksberg BS. Machine Learning in Cardiology-Ensuring Clinical Impact Lives Up to the Hype. J Cardiovasc Pharmacol Ther 2020;25:379-90. [PMID: 32495652 DOI: 10.1177/1074248420928651] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
38 Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020;2020. [PMID: 32185396 DOI: 10.1093/database/baaa010] [Cited by in Crossref: 53] [Cited by in F6Publishing: 32] [Article Influence: 53.0] [Reference Citation Analysis]
39 Nguyen D, Ngo B, vanSonnenberg E. AI in the Intensive Care Unit: Up-to-Date Review. J Intensive Care Med 2021;36:1115-23. [PMID: 32985324 DOI: 10.1177/0885066620956620] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
40 Peng Y, Nagata MH. An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. Chaos Solitons Fractals 2020;139:110055. [PMID: 32834608 DOI: 10.1016/j.chaos.2020.110055] [Cited by in Crossref: 17] [Cited by in F6Publishing: 5] [Article Influence: 8.5] [Reference Citation Analysis]
41 Eapen BR. Artificial Intelligence in Dermatology: A Practical Introduction to a Paradigm Shift. Indian Dermatol Online J 2020;11:881-9. [PMID: 33344334 DOI: 10.4103/idoj.IDOJ_388_20] [Reference Citation Analysis]
42 Arnold KF, Davies V, de Kamps M, Tennant PWG, Mbotwa J, Gilthorpe MS. Reflection on modern methods: generalized linear models for prognosis and intervention-theory, practice and implications for machine learning. Int J Epidemiol 2021;49:2074-82. [PMID: 32380551 DOI: 10.1093/ije/dyaa049] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
43 Noble PM, Appleton C, Radford AD, Nenadic G. Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs. PLoS One 2021;16:e0260402. [PMID: 34882714 DOI: 10.1371/journal.pone.0260402] [Reference Citation Analysis]
44 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] [Reference Citation Analysis]
45 Rowe TW, Katzourou IK, Stevenson-Hoare JO, Bracher-Smith MR, Ivanov DK, Escott-Price V. Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review. Brain Commun 2021;3:fcab246. [PMID: 34805994 DOI: 10.1093/braincomms/fcab246] [Reference Citation Analysis]
46 O'Leary OE, Schoetzau A, Amruthalingam L, Geber-Hollbach N, Plattner K, Jenoe P, Schmidt A, Ullmer C, Drawnel FM, Fauser S, Scholl HPN, Passweg J, Halter JP, Goldblum D. Tear Proteomic Predictive Biomarker Model for Ocular Graft Versus Host Disease Classification. Transl Vis Sci Technol 2020;9:3. [PMID: 32879760 DOI: 10.1167/tvst.9.9.3] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
47 Kumar N, Narayan Das N, Gupta D, Gupta K, Bindra J. Efficient Automated Disease Diagnosis Using Machine Learning Models. J Healthc Eng 2021;2021:9983652. [PMID: 34035886 DOI: 10.1155/2021/9983652] [Reference Citation Analysis]
48 Bougias H, Georgiadou E, Malamateniou C, Stogiannos N. Identifying cardiomegaly in chest X-rays: a cross-sectional study of evaluation and comparison between different transfer learning methods. Acta Radiol 2020;:284185120973630. [PMID: 33203215 DOI: 10.1177/0284185120973630] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
49 van Egdom LSE, Pusic A, Verhoef C, Hazelzet JA, Koppert LB. Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial. Breast J 2020;26:1213-5. [PMID: 32160651 DOI: 10.1111/tbj.13804] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
50 Farhadian M, Salemi F, Shokri A, Safi Y, Rahimpanah S. Comparison of data mining algorithms for sex determination based on mastoid process measurements using cone-beam computed tomography. Imaging Sci Dent 2020;50:323-30. [PMID: 33409141 DOI: 10.5624/isd.2020.50.4.323] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
51 Katsara MA, Branicki W, Walsh S, Kayser M, Nothnagel M; VISAGE Consortium. Evaluation of supervised machine-learning methods for predicting appearance traits from DNA. Forensic Sci Int Genet 2021;53:102507. [PMID: 33831816 DOI: 10.1016/j.fsigen.2021.102507] [Reference Citation Analysis]
52 Mujeeb Rahman KK, Subashini MM. Identification of Autism in Children Using Static Facial Features and Deep Neural Networks. Brain Sciences 2022;12:94. [DOI: 10.3390/brainsci12010094] [Reference Citation Analysis]
53 Pentrakan A, Yang CC, Wong WK. How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System? Int J Environ Res Public Health 2021;18:5523. [PMID: 34063965 DOI: 10.3390/ijerph18115523] [Reference Citation Analysis]
54 Soto-Murillo MA, Galván-Tejada JI, Galván-Tejada CE, Celaya-Padilla JM, Luna-García H, Magallanes-Quintanar R, Gutiérrez-García TA, Gamboa-Rosales H. Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods. Healthcare (Basel) 2021;9:317. [PMID: 33809283 DOI: 10.3390/healthcare9030317] [Reference Citation Analysis]
55 Rabbani N, Thornalley PJ. Reading patterns of proteome damage by glycation, oxidation and nitration: quantitation by stable isotopic dilution analysis LC-MS/MS. Essays Biochem 2020;64:169-83. [PMID: 32065835 DOI: 10.1042/EBC20190047] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
56 Zhang H, Chen D, Shao J, Zou P, Cui N, Tang L, Wang X, Wang D, Wu J, Ye Z. Machine Learning-Based Prediction for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study. Risk Manag Healthc Policy 2021;14:4361-8. [PMID: 34707419 DOI: 10.2147/RMHP.S328180] [Reference Citation Analysis]
57 Hussein T, Hammad MH, Fung PL, Al-Kloub M, Odeh I, Zaidan MA, Wraith D. COVID-19 Pandemic Development in Jordan-Short-Term and Long-Term Forecasting. Vaccines (Basel) 2021;9:728. [PMID: 34358145 DOI: 10.3390/vaccines9070728] [Reference Citation Analysis]
58 Brar K, Hachem LD, Badhiwala JH, Mau C, Zacharia BE, de Moraes FY, Pirouzmand F, Mansouri A. Management of Diffuse Low-Grade Glioma: The Renaissance of Robust Evidence. Front Oncol 2020;10:575658. [PMID: 33117714 DOI: 10.3389/fonc.2020.575658] [Reference Citation Analysis]
59 Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13(12): 1977-1990 [DOI: 10.4254/wjh.v13.i12.1977] [Reference Citation Analysis]
60 Abdolahi M, Salehi M, Shokatian I, Reiazi R. Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images. Med J Islam Repub Iran 2020;34:140. [PMID: 33437736 DOI: 10.34171/mjiri.34.140] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
61 Annapureddy AR, Angraal S, Caraballo C, Grimshaw A, Huang C, Mortazavi BJ, Krumholz HM. The National Institutes of Health funding for clinical research applying machine learning techniques in 2017. NPJ Digit Med 2020;3:13. [PMID: 32025574 DOI: 10.1038/s41746-020-0223-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
62 Bacchi S, Gluck S, Tan Y, Chim I, Cheng J, Gilbert T, Menon DK, Jannes J, Kleinig T, Koblar S. Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study. Intern Emerg Med 2020;15:989-95. [PMID: 31898204 DOI: 10.1007/s11739-019-02265-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
63 Chi S, Lee MS. Personalized Medicine Using Neuroimmunological Biomarkers in Depressive Disorders. J Pers Med 2021;11:114. [PMID: 33578686 DOI: 10.3390/jpm11020114] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
64 Bellini V, Guzzon M, Bigliardi B, Mordonini M, Filippelli S, Bignami E. Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization. J Med Syst 2019;44:20. [PMID: 31823034 DOI: 10.1007/s10916-019-1512-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
65 Moll M, Qiao D, Regan EA, Hunninghake GM, Make BJ, Tal-Singer R, McGeachie MJ, Castaldi PJ, San Jose Estepar R, Washko GR, Wells JM, LaFon D, Strand M, Bowler RP, Han MK, Vestbo J, Celli B, Calverley P, Crapo J, Silverman EK, Hobbs BD, Cho MH. Machine Learning and Prediction of All-Cause Mortality in COPD. Chest 2020;158:952-64. [PMID: 32353417 DOI: 10.1016/j.chest.2020.02.079] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
66 Laoveeravat P, Abhyankar PR, Brenner AR, Gabr MM, Habr FG, Atsawarungruangkit A. Artificial intelligence for pancreatic cancer detection: Recent development and future direction . Artif Intell Gastroenterol 2021; 2(2): 56-68 [DOI: 10.35712/aig.v2.i2.56] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
67 Lee A, Weintraub S, Xi IL, Ahn J, Bernstein J. Predicting life expectancy after geriatric hip fracture: A systematic review. PLoS One 2021;16:e0261279. [PMID: 34910791 DOI: 10.1371/journal.pone.0261279] [Reference Citation Analysis]
68 García-Pola M, Pons-Fuster E, Suárez-Fernández C, Seoane-Romero J, Romero-Méndez A, López-Jornet P. Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review. Cancers (Basel) 2021;13:4600. [PMID: 34572831 DOI: 10.3390/cancers13184600] [Reference Citation Analysis]
69 Gonçalves DM, Henriques R, Costa RS. Predicting Postoperative Complications in Cancer Patients: A Survey Bridging Classical and Machine Learning Contributions to Postsurgical Risk Analysis. Cancers (Basel) 2021;13:3217. [PMID: 34203189 DOI: 10.3390/cancers13133217] [Reference Citation Analysis]
70 Feofanova EV, Zhang GQ, Lhatoo S, Metcalf GA, Boerwinkle E, Venner E. The Implementation Science for Genomic Health Translation (INSIGHT) Study in Epilepsy: Protocol for a Learning Health Care System. JMIR Res Protoc 2021;10:e25576. [PMID: 33769305 DOI: 10.2196/25576] [Reference Citation Analysis]
71 Mattei F, Andreone S, Mencattini A, De Ninno A, Businaro L, Martinelli E, Schiavoni G. Oncoimmunology Meets Organs-on-Chip. Front Mol Biosci 2021;8:627454. [PMID: 33842539 DOI: 10.3389/fmolb.2021.627454] [Reference Citation Analysis]
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