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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: 84] [Article Influence: 87.5] [Reference Citation Analysis]
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1 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: 5.0] [Reference Citation Analysis]
2 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]
3 Rema J, Novais F, Telles-Correia D. Precision Psychiatry: Machine learning as a tool to find new pharmacological targets. Curr Top Med Chem 2021. [PMID: 34607546 DOI: 10.2174/1568026621666211004095917] [Reference Citation Analysis]
4 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: 4] [Article Influence: 2.5] [Reference Citation Analysis]
5 French MA, Roemmich RT, Daley K, Beier M, Penttinen S, Raghavan P, Searson P, Wegener S, Celnik P. Precision rehabilitation: optimizing function, adding value to health care. Arch Phys Med Rehabil 2022:S0003-9993(22)00213-1. [PMID: 35181267 DOI: 10.1016/j.apmr.2022.01.154] [Reference Citation Analysis]
6 Nolsøe AB, Østergren PB, Jensen CFS, Fode M. From separation to collaboration: the future of urology. Nat Rev Urol 2019;16:633-4. [PMID: 31575989 DOI: 10.1038/s41585-019-0241-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
7 Lin HM, Xue XF, Wang XG, Dang SC, Gu M. Application of artificial intelligence for the diagnosis, treatment, and prognosis of pancreatic cancer. Artif Intell Gastroenterol 2020; 1(1): 19-29 [DOI: 10.35712/aig.v1.i1.19] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
8 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]
9 de Miguel I, Sanz B, Lazcoz G. Machine learning in the EU health care context: exploring the ethical, legal and social issues. Information, Communication & Society 2020;23:1139-53. [DOI: 10.1080/1369118x.2020.1719185] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
10 Dall'Alba G, Casa PL, Abreu FP, Notari DL, de Avila E Silva S. A Survey of Biological Data in a Big Data Perspective. Big Data 2022. [PMID: 35394342 DOI: 10.1089/big.2020.0383] [Reference Citation Analysis]
11 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: 4] [Article Influence: 2.0] [Reference Citation Analysis]
12 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]
13 Pannakkong W, Thiwa-anont K, Singthong K, Parthanadee P, Buddhakulsomsiri J, Chang K. Hyperparameter Tuning of Machine Learning Algorithms Using Response Surface Methodology: A Case Study of ANN, SVM, and DBN. Mathematical Problems in Engineering 2022;2022:1-17. [DOI: 10.1155/2022/8513719] [Reference Citation Analysis]
14 Bartold PM, Ivanovski S. P4 Medicine as a model for precision periodontal care. Clin Oral Investig 2022. [PMID: 35344104 DOI: 10.1007/s00784-022-04469-y] [Reference Citation Analysis]
15 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: 32] [Article Influence: 20.0] [Reference Citation Analysis]
16 Alam Khan Z, Feng Z, Uddin MI, Mast N, Ali Shah SA, Imtiaz M, Al-khasawneh MA, Mahmoud M, Ali S. Optimal Policy Learning for Disease Prevention Using Reinforcement Learning. Scientific Programming 2020;2020:1-13. [DOI: 10.1155/2020/7627290] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Tang H, Yu X, Liu R, Zeng T. Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion. Brief Bioinform 2022:bbab584. [PMID: 35106553 DOI: 10.1093/bib/bbab584] [Reference Citation Analysis]
18 Nwanosike EM, Conway BR, Merchant HA, Hasan SS. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. Int J Med Inform 2021;159:104679. [PMID: 34990939 DOI: 10.1016/j.ijmedinf.2021.104679] [Reference Citation Analysis]
19 Gumbs AA, Frigerio I, Spolverato G, Croner R, Illanes A, Chouillard E, Elyan E. Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery? Sensors (Basel) 2021;21:5526. [PMID: 34450976 DOI: 10.3390/s21165526] [Reference Citation Analysis]
20 Chong Y, Wu Y, Liu J, Han C, Gong L, Liu X, Liang N, Li S. Clinicopathological models for predicting lymph node metastasis in patients with early-stage lung adenocarcinoma: the application of machine learning algorithms. J Thorac Dis 2021;13:4033-42. [PMID: 34422333 DOI: 10.21037/jtd-21-98] [Reference Citation Analysis]
21 Sealfon RSG, Wong AK, Troyanskaya OG. Machine learning methods to model multicellular complexity and tissue specificity. Nat Rev Mater 2021;6:717-29. [DOI: 10.1038/s41578-021-00339-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
22 Kumar A, Parihar A, Panda U, Parihar DS. Microfluidics-Based Point-of-Care Testing (POCT) Devices in Dealing with Waves of COVID-19 Pandemic: The Emerging Solution. ACS Appl Bio Mater 2022. [PMID: 35473316 DOI: 10.1021/acsabm.1c01320] [Reference Citation Analysis]
23 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]
24 Oei RW, Lyu Y, Ye L, Kong F, Du C, Zhai R, Xu T, Shen C, He X, Kong L, Hu C, Ying H. Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics. J Pers Med 2021;11:787. [PMID: 34442430 DOI: 10.3390/jpm11080787] [Reference Citation Analysis]
25 Cui R, Hua W, Qu K, Yang H, Tong Y, Li Q, Wang H, Ma Y, Liu S, Lin T, Zhang J, Sun J, Liu C. An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning. Front Med (Lausanne) 2021;8:775047. [PMID: 34926518 DOI: 10.3389/fmed.2021.775047] [Reference Citation Analysis]
26 Marti-Bonmati L, Koh DM, Riklund K, Bobowicz M, Roussakis Y, Vilanova JC, Fütterer JJ, Rimola J, Mallol P, Ribas G, Miguel A, Tsiknakis M, Lekadir K, Tsakou G. Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper. Insights Imaging 2022;13:89. [PMID: 35536446 DOI: 10.1186/s13244-022-01220-9] [Reference Citation Analysis]
27 Tayebi Z, Ali S, Patterson M. Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants. Algorithms 2021;14:348. [DOI: 10.3390/a14120348] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 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: 0.5] [Reference Citation Analysis]
29 Lin H, Xue X, Wang X, Dang S, Gu M. Application of artificial intelligence for the diagnosis, treatment, and prognosis of pancreatic cancer. AIG 2020;1:19-29. [DOI: 10.35712/wjg.v1.i1.19] [Reference Citation Analysis]
30 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: 2.0] [Reference Citation Analysis]
31 Simfukwe C, An SS, Youn YC. Comparison of RCF Scoring System to Clinical Decision for the Rey Complex Figure Using Machine-Learning Algorithm. Dement Neurocogn Disord 2021;20:70-9. [PMID: 34795770 DOI: 10.12779/dnd.2021.20.4.70] [Reference Citation Analysis]
32 Alexander JC, Romito BT, Çobanoğlu MC. The present and future role of artificial intelligence and machine learning in anesthesiology. Int Anesthesiol Clin 2020;58:7-16. [PMID: 32841964 DOI: 10.1097/AIA.0000000000000294] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
33 Hu M, Asami C, Iwakura H, Nakajima Y, Sema R, Kikuchi T, Miyata T, Sakamaki K, Kudo T, Yamada M, Akamizu T, Sakakibara Y. Development and preliminary validation of a machine learning system for thyroid dysfunction diagnosis based on routine laboratory tests. Commun Med 2022;2. [DOI: 10.1038/s43856-022-00071-1] [Reference Citation Analysis]
34 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]
35 Ji W, Xue M, Zhang Y, Yao H, Wang Y. A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population. Front Public Health 2022;10:846118. [DOI: 10.3389/fpubh.2022.846118] [Reference Citation Analysis]
36 Ng WY, Tan TE, Movva PVH, Fang AHS, Yeo KK, Ho D, Foo FSS, Xiao Z, Sun K, Wong TY, Sia AT, Ting DSW. Blockchain applications in health care for COVID-19 and beyond: a systematic review. Lancet Digit Health 2021;3:e819-29. [PMID: 34654686 DOI: 10.1016/S2589-7500(21)00210-7] [Reference Citation Analysis]
37 Wu Y, Zhang Q, Hu Y, Sun-woo K, Zhang X, Zhu H, jie L, Li S. Novel binary logistic regression model based on feature transformation of XGBoost for type 2 Diabetes Mellitus prediction in healthcare systems. Future Generation Computer Systems 2022;129:1-12. [DOI: 10.1016/j.future.2021.11.003] [Reference Citation Analysis]
38 Sadik O, Schaffer D, Land W, Xue H, Yazgan I, Kafesçilere AK, Sungur M. A Bayesian Network Concept for Pain Assessment (Preprint). JMIR Biomedical Engineering. [DOI: 10.2196/35711] [Reference Citation Analysis]
39 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] [Reference Citation Analysis]
40 Haller S, Van Cauter S, Federau C, Hedderich DM, Edjlali M. The R-AI-DIOLOGY checklist: a practical checklist for evaluation of artificial intelligence tools in clinical neuroradiology. Neuroradiology. [DOI: 10.1007/s00234-021-02890-w] [Reference Citation Analysis]
41 Adomavicius G, Yang M. Integrating Behavioral, Economic, and Technical Insights to Understand and Address Algorithmic Bias: A Human-Centric Perspective. ACM Trans Manage Inf Syst . [DOI: 10.1145/3519420] [Reference Citation Analysis]
42 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]
43 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: 2.0] [Reference Citation Analysis]
44 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: 2] [Article Influence: 1.0] [Reference Citation Analysis]
45 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]
46 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: 3] [Article Influence: 2.0] [Reference Citation Analysis]
47 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: 1.5] [Reference Citation Analysis]
48 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]
49 Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review. J Med Internet Res 2022;24:e32215. [PMID: 35084349 DOI: 10.2196/32215] [Reference Citation Analysis]
50 Mittas N, Chatzopoulou F, Kyritsis KA, Papagiannopoulos CI, Theodoroula NF, Papazoglou AS, Karagiannidis E, Sofidis G, Moysidis DV, Stalikas N, Papa A, Chatzidimitriou D, Sianos G, Angelis L, Vizirianakis IS. A Risk-Stratification Machine Learning Framework for the Prediction of Coronary Artery Disease Severity: Insights From the GESS Trial. Front Cardiovasc Med 2022;8:812182. [DOI: 10.3389/fcvm.2021.812182] [Reference Citation Analysis]
51 Zeina M, Balston A, Banerjee A, Woolf K. Gender and ethnic differences in publication of BMJ letters to the editor: an observational study using machine learning. BMJ Open 2020;10:e037269. [PMID: 33371013 DOI: 10.1136/bmjopen-2020-037269] [Reference Citation Analysis]
52 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]
53 Allareddy V, Lee MK, Vaid NR, Yadav S. Use of Neural Network model to examine post-operative infections following orthognathic surgeries in the United States. Seminars in Orthodontics 2021;27:130-7. [DOI: 10.1053/j.sodo.2021.05.009] [Reference Citation Analysis]
54 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: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
55 Attallah O. DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity. Diagnostics (Basel) 2021;11:2034. [PMID: 34829380 DOI: 10.3390/diagnostics11112034] [Reference Citation Analysis]
56 Aldahiri A, Alrashed B, Hussain W. Trends in Using IoT with Machine Learning in Health Prediction System. Forecasting 2021;3:181-206. [DOI: 10.3390/forecast3010012] [Cited by in Crossref: 10] [Cited by in F6Publishing: 1] [Article Influence: 10.0] [Reference Citation Analysis]
57 Ullah Z, Al-turjman F, Mostarda L, Gagliardi R. Applications of Artificial Intelligence and Machine learning in smart cities. Computer Communications 2020;154:313-23. [DOI: 10.1016/j.comcom.2020.02.069] [Cited by in Crossref: 107] [Article Influence: 53.5] [Reference Citation Analysis]
58 Maini B, Maini E. Artificial Intelligence in Medical Education. Indian Pediatr 2021;58:496-7. [DOI: 10.1007/s13312-021-2228-0] [Reference Citation Analysis]
59 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]
60 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: 6] [Article Influence: 4.0] [Reference Citation Analysis]
61 Ghaithan AM, Alarfaj I, Mohammed A, Qasim O. A neural network-based model for estimating the delivery time of oxygen gas cylinders during COVID-19 pandemic. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07037-3] [Reference Citation Analysis]
62 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: 4] [Article Influence: 2.5] [Reference Citation Analysis]
63 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: 6] [Article Influence: 16.5] [Reference Citation Analysis]
64 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]
65 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]
66 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]
67 Liu X, Lu D, Zhang A, Liu Q, Jiang G. Data-Driven Machine Learning in Environmental Pollution: Gains and Problems. Environ Sci Technol 2022. [PMID: 35084840 DOI: 10.1021/acs.est.1c06157] [Reference Citation Analysis]
68 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: 2.0] [Reference Citation Analysis]
69 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]
70 Bose SN, Greenstein JL, Fackler JC, Sarma SV, Winslow RL, Bembea MM. Early Prediction of Multiple Organ Dysfunction in the Pediatric Intensive Care Unit. Front Pediatr 2021;9:711104. [PMID: 34485201 DOI: 10.3389/fped.2021.711104] [Reference Citation Analysis]
71 Seguin L, Tassy L. E-santé, digitalisation ou transformation numérique : impact sur les soins de support en oncologie. Bulletin du Cancer 2022. [DOI: 10.1016/j.bulcan.2021.08.015] [Reference Citation Analysis]
72 Nong P, Williamson A, Anthony D, Platt J, Kardia S. Discrimination, trust, and withholding information from providers: Implications for missing data and inequity. SSM - Population Health 2022;18:101092. [DOI: 10.1016/j.ssmph.2022.101092] [Reference Citation Analysis]
73 Ho WK, Tang B, Wong SW. Predicting property prices with machine learning algorithms. Journal of Property Research 2021;38:48-70. [DOI: 10.1080/09599916.2020.1832558] [Cited by in Crossref: 15] [Cited by in F6Publishing: 3] [Article Influence: 7.5] [Reference Citation Analysis]
74 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]
75 Roberts TJ, Lennes IT. Lessons for Oncology From the COVID-19 Pandemic: Operationalizing and Scaling Virtual Cancer Care in Health Systems. Cancer J 2022;28:125-33. [PMID: 35333498 DOI: 10.1097/PPO.0000000000000579] [Reference Citation Analysis]
76 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]
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