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For: Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20:e262-e273. [PMID: 31044724 DOI: 10.1016/s1470-2045(19)30149-4] [Cited by in Crossref: 175] [Cited by in F6Publishing: 70] [Article Influence: 175.0] [Reference Citation Analysis]
Number Citing Articles
1 Kardas P, Aguilar-Palacio I, Almada M, Cahir C, Costa E, Giardini A, Malo S, Massot Mesquida M, Menditto E, Midão L, Parra-Calderón CL, Pepiol Salom E, Vrijens B. The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint. J Med Internet Res 2020;22:e18150. [PMID: 32663138 DOI: 10.2196/18150] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
2 Zhong J, Liu P, Li S, Huang X, Zhang Q, Huang J, Guo Y, Chen M, Ruan Z, Qin C, Xu L. A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling. J Healthc Eng 2020;2020:8847144. [PMID: 32802300 DOI: 10.1155/2020/8847144] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
3 Qiu J, Li P, Dong M, Xin X, Tan J. Personalized prediction of live birth prior to the first in vitro fertilization treatment: a machine learning method. J Transl Med 2019;17:317. [PMID: 31547822 DOI: 10.1186/s12967-019-2062-5] [Cited by in Crossref: 15] [Cited by in F6Publishing: 7] [Article Influence: 7.5] [Reference Citation Analysis]
4 Rajula HSR, Verlato G, Manchia M, Antonucci N, Fanos V. Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment. Medicina (Kaunas) 2020;56:E455. [PMID: 32911665 DOI: 10.3390/medicina56090455] [Cited by in Crossref: 13] [Cited by in F6Publishing: 6] [Article Influence: 13.0] [Reference Citation Analysis]
5 Raja R, Mukherjee I, Sarkar BK. A Machine Learning-Based Prediction Model for Preterm Birth in Rural India. J Healthc Eng 2021;2021:6665573. [PMID: 34234931 DOI: 10.1155/2021/6665573] [Reference Citation Analysis]
6 Gilhodes J, Dalenc F, Gal J, Zemmour C, Leconte E, Boher JM, Filleron T. Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings. Comput Math Methods Med 2020;2020:6795392. [PMID: 32670394 DOI: 10.1155/2020/6795392] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Doyle PW, Kavoussi NL. Machine learning applications to enhance patient specific care for urologic surgery. World J Urol 2021. [PMID: 34047826 DOI: 10.1007/s00345-021-03738-x] [Reference Citation Analysis]
8 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: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
9 Huang Y, Talwar A, Chatterjee S, Aparasu RR. Application of machine learning in predicting hospital readmissions: a scoping review of the literature. BMC Med Res Methodol 2021;21:96. [PMID: 33952192 DOI: 10.1186/s12874-021-01284-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Moncada-Torres A, van Maaren MC, Hendriks MP, Siesling S, Geleijnse G. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci Rep 2021;11:6968. [PMID: 33772109 DOI: 10.1038/s41598-021-86327-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
11 Martinez-Martin N, Luo Z, Kaushal A, Adeli E, Haque A, Kelly SS, Wieten S, Cho MK, Magnus D, Fei-Fei L, Schulman K, Milstein A. Ethical issues in using ambient intelligence in health-care settings. Lancet Digit Health 2021;3:e115-23. [PMID: 33358138 DOI: 10.1016/S2589-7500(20)30275-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
12 Wu Y, Rao K, Liu J, Han C, Gong L, Chong Y, Liu Z, Xu X. Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer. Front Endocrinol (Lausanne) 2020;11:577537. [PMID: 33193092 DOI: 10.3389/fendo.2020.577537] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
13 Wu Y, Liu J, Han C, Liu X, Chong Y, Wang Z, Gong L, Zhang J, Gao X, Guo C, Liang N, Li S. Preoperative Prediction of Lymph Node Metastasis in Patients With Early-T-Stage Non-small Cell Lung Cancer by Machine Learning Algorithms. Front Oncol 2020;10:743. [PMID: 32477952 DOI: 10.3389/fonc.2020.00743] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
14 Li C, Yu H, Sun Y, Zeng X, Zhang W. Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis. PeerJ. 2021;9:e10682. [PMID: 33717664 DOI: 10.7717/peerj.10682] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
15 Kaur N, Bhattacharya S, Butte AJ. Big Data in Nephrology. Nat Rev Nephrol 2021. [PMID: 34194006 DOI: 10.1038/s41581-021-00439-x] [Reference Citation Analysis]
16 Huang C, Xiang Z, Zhang Y, Tan DS, Yip CK, Liu Z, Li Y, Yu S, Diao L, Wong LY, Ling WL, Zeng Y, Tu W. Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients. Front Immunol 2021;12:642167. [PMID: 33868275 DOI: 10.3389/fimmu.2021.642167] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 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: 28] [Article Influence: 53.0] [Reference Citation Analysis]
18 Wang QC, Wang ZY. Big Data and Atrial Fibrillation: Current Understanding and New Opportunities. J Cardiovasc Transl Res 2020;13:944-52. [PMID: 32378163 DOI: 10.1007/s12265-020-10008-5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
19 Fletcher RR, Nakeshimana A, Olubeko O. Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health. Front Artif Intell 2020;3:561802. [PMID: 33981989 DOI: 10.3389/frai.2020.561802] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Wu D, Yang Q, Su B, Hao J, Ma H, Yuan W, Gao J, Ding F, Xu Y, Wang H, Zhao J, Li B. Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development. Front Cardiovasc Med 2021;8:619386. [PMID: 33937355 DOI: 10.3389/fcvm.2021.619386] [Reference Citation Analysis]
21 Kisch T, Stang FH, Mailaender P, Schleusser S, Michel D, Trieb R, Bannwarth S, Maly S, Dallmann A, Klasen S, Kaiser C, Schmeltzpfenning T, Rempp W, Lades M, Šurc D, Bauer B, Artschwager A, Vonthein R. Smart Scar Care-Industry 4.0 in Individualized Compression Garments: A Randomized Controlled Crossover Feasibility Study. Plast Reconstr Surg Glob Open 2021;9:e3683. [PMID: 34367847 DOI: 10.1097/GOX.0000000000003683] [Reference Citation Analysis]
22 Li M, Xie S, Lu C, Zhu L, Zhu L. Application of Data Science in Circulating Tumor DNA Detection: A Promising Avenue Towards Liquid Biopsy. Front Oncol 2021;11:692322. [PMID: 34367974 DOI: 10.3389/fonc.2021.692322] [Reference Citation Analysis]
23 Taguchi YH, Turki T. Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data. Genes (Basel) 2020;11:E1493. [PMID: 33322492 DOI: 10.3390/genes11121493] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
24 Golse N. AI finally provides augmented intelligence to liver surgeons. EBioMedicine 2020;61:103064. [PMID: 33096474 DOI: 10.1016/j.ebiom.2020.103064] [Reference Citation Analysis]
25 Borges do Nascimento IJ, Marcolino MS, Abdulazeem HM, Weerasekara I, Azzopardi-Muscat N, Gonçalves MA, Novillo-Ortiz D. Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies. J Med Internet Res 2021;23:e27275. [PMID: 33847586 DOI: 10.2196/27275] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Liu S, See KC, Ngiam KY, Celi LA, Sun X, Feng M. Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review. J Med Internet Res 2020;22:e18477. [PMID: 32706670 DOI: 10.2196/18477] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 10.0] [Reference Citation Analysis]
27 Deng W, Yang B, Liu W, Song W, Gao Y, Xu J. CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network. Comput Math Methods Med 2021;2021:7259414. [PMID: 34335865 DOI: 10.1155/2021/7259414] [Reference Citation Analysis]
28 Giacobbe DR, Mora S, Giacomini M, Bassetti M. Machine Learning and Multidrug-Resistant Gram-Negative Bacteria: An Interesting Combination for Current and Future Research. Antibiotics (Basel) 2020;9:E54. [PMID: 32023986 DOI: 10.3390/antibiotics9020054] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
29 Xue M, Su Y, Feng Z, Wang S, Zhang M, Wang K, Yao H. A nomogram model for screening the risk of diabetes in a large-scale Chinese population: an observational study from 345,718 participants. Sci Rep 2020;10:11600. [PMID: 32665620 DOI: 10.1038/s41598-020-68383-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
30 Nakajo M, Jinguji M, Tani A, Kikuno H, Hirahara D, Togami S, Kobayashi H, Yoshiura T. Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer. Mol Imaging Biol 2021;23:756-65. [PMID: 33763816 DOI: 10.1007/s11307-021-01599-9] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
31 Tezza F, Lorenzoni G, Azzolina D, Barbar S, Leone LAC, Gregori D. Predicting in-Hospital Mortality of Patients with COVID-19 Using Machine Learning Techniques. J Pers Med 2021;11:343. [PMID: 33923332 DOI: 10.3390/jpm11050343] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
32 Kazançoğlu Y, Sağnak M, Lafcı Ç, Luthra S, Kumar A, Taçoğlu C. Big Data-Enabled Solutions Framework to Overcoming the Barriers to Circular Economy Initiatives in Healthcare Sector. Int J Environ Res Public Health 2021;18:7513. [PMID: 34299964 DOI: 10.3390/ijerph18147513] [Reference Citation Analysis]
33 Liu G, Gao Y, Liu Y, Guo Y, Yan Z, Ou Z, Zhong L, Xie C, Zeng J, Zhang W, Peng K, Lv Q. Machine Learning for Predicting Individual Severity of Blepharospasm Using Diffusion Tensor Imaging. Front Neurosci 2021;15:670475. [PMID: 34054417 DOI: 10.3389/fnins.2021.670475] [Reference Citation Analysis]
34 Cheng X, Lin SY, Liu J, Liu S, Zhang J, Nie P, Fuemmeler BF, Wang Y, Xue H. Does Physical Activity Predict Obesity-A Machine Learning and Statistical Method-Based Analysis. Int J Environ Res Public Health 2021;18:3966. [PMID: 33918760 DOI: 10.3390/ijerph18083966] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Wu WT, Li YJ, Feng AZ, Li L, Huang T, Xu AD, Lyu J. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res 2021;8:44. [PMID: 34380547 DOI: 10.1186/s40779-021-00338-z] [Reference Citation Analysis]
36 Högqvist Tabor V, Högqvist Tabor M, Keestra S, Parrot JE, Alvergne A. Improving the Quality of Life of Patients with an Underactive Thyroid Through mHealth: A Patient-Centered Approach. Womens Health Rep (New Rochelle) 2021;2:182-94. [PMID: 34235505 DOI: 10.1089/whr.2021.0010] [Reference Citation Analysis]
37 Xue M, Su Y, Li C, Wang S, Yao H. Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework. J Diabetes Res 2020;2020:6873891. [PMID: 33029536 DOI: 10.1155/2020/6873891] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
38 de Marvao A, Dawes TJW, O'Regan DP. Artificial Intelligence for Cardiac Imaging-Genetics Research. Front Cardiovasc Med 2019;6:195. [PMID: 32039240 DOI: 10.3389/fcvm.2019.00195] [Cited by in Crossref: 8] [Cited by in F6Publishing: 3] [Article Influence: 8.0] [Reference Citation Analysis]
39 Merenda M, Porcaro C, Iero D. Edge Machine Learning for AI-Enabled IoT Devices: A Review. Sensors (Basel) 2020;20:E2533. [PMID: 32365645 DOI: 10.3390/s20092533] [Cited by in Crossref: 33] [Cited by in F6Publishing: 3] [Article Influence: 33.0] [Reference Citation Analysis]
40 Baumann M, Ebert N, Kurth I, Bacchus C, Overgaard J. What will radiation oncology look like in 2050? A look at a changing professional landscape in Europe and beyond. Mol Oncol 2020;14:1577-85. [PMID: 32463984 DOI: 10.1002/1878-0261.12731] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
41 Nam JG, Witanto JN, Park SJ, Yoo SJ, Goo JM, Yoon SH. Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel maps. Eur Radiol 2021. [PMID: 34009411 DOI: 10.1007/s00330-021-08036-z] [Reference Citation Analysis]
42 Guan Y, Cheng CH, Chen W, Zhang Y, Koo S, Krengel M, Janulewicz P, Toomey R, Yang E, Bhadelia R, Steele L, Kim JH, Sullivan K, Koo BB. Neuroimaging Markers for Studying Gulf-War Illness: Single-Subject Level Analytical Method Based on Machine Learning. Brain Sci 2020;10:E884. [PMID: 33233672 DOI: 10.3390/brainsci10110884] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
43 Wolff J, Gary A, Jung D, Normann C, Kaier K, Binder H, Domschke K, Klimke A, Franz M. Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach. BMC Med Inform Decis Mak 2020;20:21. [PMID: 32028934 DOI: 10.1186/s12911-020-1042-2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
44 Kiely DG, Doyle O, Drage E, Jenner H, Salvatelli V, Daniels FA, Rigg J, Schmitt C, Samyshkin Y, Lawrie A, Bergemann R. Utilising artificial intelligence to determine patients at risk of a rare disease: idiopathic pulmonary arterial hypertension. Pulm Circ 2019;9:2045894019890549. [PMID: 31798836 DOI: 10.1177/2045894019890549] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
45 Challa AP, Beam AL, Shen M, Peryea T, Lavieri RR, Lippmann ES, Aronoff DM. Machine learning on drug-specific data to predict small molecule teratogenicity. Reprod Toxicol 2020;95:148-58. [PMID: 32428651 DOI: 10.1016/j.reprotox.2020.05.004] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
46 Chin YPH, Song W, Lien CE, Yoon CH, Wang WC, Liu J, Nguyen PA, Feng YT, Zhou L, Li YCJ, Bates DW. Assessing the International Transferability of a Machine Learning Model for Detecting Medication Error in the General Internal Medicine Clinic: Multicenter Preliminary Validation Study. JMIR Med Inform 2021;9:e23454. [PMID: 33502331 DOI: 10.2196/23454] [Reference Citation Analysis]
47 Lustgarten JL, Zehnder A, Shipman W, Gancher E, Webb TL. Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA). JAMIA Open 2020;3:306-17. [PMID: 32734172 DOI: 10.1093/jamiaopen/ooaa005] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
48 Ji J, Hu L, Liu B, Li Y. Identifying and assessing the impact of key neighborhood-level determinants on geographic variation in stroke: a machine learning and multilevel modeling approach. BMC Public Health 2020;20:1666. [PMID: 33160324 DOI: 10.1186/s12889-020-09766-3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
49 Zippel C, Bohnet-Joschko S. Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov. Int J Environ Res Public Health 2021;18:5072. [PMID: 34064827 DOI: 10.3390/ijerph18105072] [Reference Citation Analysis]
50 Xue M, Liu L, Wang S, Su Y, Lv K, Zhang M, Yao H. A simple nomogram score for screening patients with type 2 diabetes to detect those with hypertension: A cross-sectional study based on a large community survey in China. PLoS One 2020;15:e0236957. [PMID: 32764769 DOI: 10.1371/journal.pone.0236957] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
51 Wu Y, Hu H, Cai J, Chen R, Zuo X, Cheng H, Yan D. Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults. Front Public Health 2021;9:626331. [PMID: 34268283 DOI: 10.3389/fpubh.2021.626331] [Reference Citation Analysis]
52 Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D, Ashrafian H, Darzi A. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med 2021;4:65. [PMID: 33828217 DOI: 10.1038/s41746-021-00438-z] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
53 Comoretto RI, Azzolina D, Amigoni A, Stoppa G, Todino F, Wolfler A, Gregori D, On Behalf Of The TIPNet Study Group. Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques. Diagnostics (Basel) 2021;11:1299. [PMID: 34359385 DOI: 10.3390/diagnostics11071299] [Reference Citation Analysis]
54 Feng C, Xiang T, Yi Z, Meng X, Chu X, Huang G, Zhao X, Chen F, Xiong B, Feng J. A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma. Front Oncol 2021;11:653863. [PMID: 34336652 DOI: 10.3389/fonc.2021.653863] [Reference Citation Analysis]
55 Ho D, Quake SR, McCabe ERB, Chng WJ, Chow EK, Ding X, Gelb BD, Ginsburg GS, Hassenstab J, Ho CM, Mobley WC, Nolan GP, Rosen ST, Tan P, Yen Y, Zarrinpar A. Enabling Technologies for Personalized and Precision Medicine. Trends Biotechnol 2020;38:497-518. [PMID: 31980301 DOI: 10.1016/j.tibtech.2019.12.021] [Cited by in Crossref: 40] [Cited by in F6Publishing: 27] [Article Influence: 40.0] [Reference Citation Analysis]
56 Fridrichova I, Kalinkova L, Karhanek M, Smolkova B, Machalekova K, Wachsmannova L, Nikolaieva N, Kajo K. miR-497-5p Decreased Expression Associated with High-Risk Endometrial Cancer. Int J Mol Sci 2020;22:E127. [PMID: 33374439 DOI: 10.3390/ijms22010127] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
57 Iqbal MJ, Javed Z, Sadia H, Qureshi IA, Irshad A, Ahmed R, Malik K, Raza S, Abbas A, Pezzani R, Sharifi-Rad J. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell Int 2021;21:270. [PMID: 34020642 DOI: 10.1186/s12935-021-01981-1] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
58 Park Y, Heider D, Hauschild AC. Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence. Cancers (Basel) 2021;13:3148. [PMID: 34202427 DOI: 10.3390/cancers13133148] [Reference Citation Analysis]
59 Liang X, Wang Z, Dai Z, Zhang H, Cheng Q, Liu Z. Promoting Prognostic Model Application: A Review Based on Gliomas. J Oncol 2021;2021:7840007. [PMID: 34394352 DOI: 10.1155/2021/7840007] [Reference Citation Analysis]
60 Yin J, Ngiam KY, Teo HH. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. J Med Internet Res 2021;23:e25759. [PMID: 33885365 DOI: 10.2196/25759] [Reference Citation Analysis]
61 Zhu J, Zheng J, Li L, Huang R, Ren H, Wang D, Dai Z, Su X. Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma. Front Med (Lausanne) 2021;8:635771. [PMID: 33768105 DOI: 10.3389/fmed.2021.635771] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
62 Liu Q, Pang B, Li H, Zhang B, Liu Y, Lai L, Le W, Li J, Xia T, Zhang X, Ou C, Ma J, Li S, Guo X, Zhang S, Zhang Q, Jiang M, Zeng Q. Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia. J Thorac Dis 2021;13:1215-29. [PMID: 33717594 DOI: 10.21037/jtd-20-2580] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
63 Tan J, Qin F, Yuan J. Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment. Transl Androl Urol 2021;10:1769-79. [PMID: 33968664 DOI: 10.21037/tau-20-1405] [Reference Citation Analysis]
64 Giacobbe DR, Signori A, Del Puente F, Mora S, Carmisciano L, Briano F, Vena A, Ball L, Robba C, Pelosi P, Giacomini M, Bassetti M. Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective. Front Med (Lausanne) 2021;8:617486. [PMID: 33644097 DOI: 10.3389/fmed.2021.617486] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
65 van den Broek AG, Kloek CJJ, Pisters MF, Veenhof C. Validity and reliability of the Dutch STarT MSK tool in patients with musculoskeletal pain in primary care physiotherapy. PLoS One 2021;16:e0248616. [PMID: 33735303 DOI: 10.1371/journal.pone.0248616] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
66 Lu J, Deng K, Zhang X, Liu G, Guan Y. Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens. iScience 2021;24:102804. [PMID: 34308294 DOI: 10.1016/j.isci.2021.102804] [Reference Citation Analysis]
67 Han C, Liu J, Wu Y, Chong Y, Chai X, Weng X. To Predict the Length of Hospital Stay After Total Knee Arthroplasty in an Orthopedic Center in China: The Use of Machine Learning Algorithms. Front Surg 2021;8:606038. [PMID: 33777997 DOI: 10.3389/fsurg.2021.606038] [Reference Citation Analysis]
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