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For: Corey KM, Kashyap S, Lorenzi E, Lagoo-Deenadayalan SA, Heller K, Whalen K, Balu S, Heflin MT, McDonald SR, Swaminathan M, Sendak M. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study. PLoS Med 2018;15:e1002701. [PMID: 30481172 DOI: 10.1371/journal.pmed.1002701] [Cited by in Crossref: 62] [Cited by in F6Publishing: 66] [Article Influence: 15.5] [Reference Citation Analysis]
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25 van de Sande D, van Genderen ME, Verhoef C, van Bommel J, Gommers D, van Unen E, Huiskens J, Grünhagen DJ. Predicting need for hospital-specific interventional care after surgery using electronic health record data. Surgery 2021;170:790-6. [PMID: 34090676 DOI: 10.1016/j.surg.2021.05.005] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
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27 Guo S, Zhao B, An Y, Zhang Y, Meng Z, Zhou Y, Zheng M, Yang D, Wang M, Ying B. Potential Fluid Biomarkers and a Prediction Model for Better Recognition Between Multiple System Atrophy-Cerebellar Type and Spinocerebellar Ataxia. Front Aging Neurosci 2021;13:644699. [PMID: 33958996 DOI: 10.3389/fnagi.2021.644699] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
28 Kansal A, Gao M, Balu S, Nichols M, Corey K, Kashyap S, Sendak M. Impact of diagnosis code grouping method on clinical prediction model performance: A multi-site retrospective observational study. Int J Med Inform 2021;151:104466. [PMID: 33933904 DOI: 10.1016/j.ijmedinf.2021.104466] [Reference Citation Analysis]
29 Falconer N, Abdel-Hafez A, Scott IA, Marxen S, Canaris S, Barras M. Systematic review of machine learning models for personalised dosing of heparin. Br J Clin Pharmacol 2021. [PMID: 33835524 DOI: 10.1111/bcp.14852] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
30 Møller JK, Sørensen M, Hardahl C. Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study. PLoS One 2021;16:e0248636. [PMID: 33788888 DOI: 10.1371/journal.pone.0248636] [Reference Citation Analysis]
31 Greenbury SF, Ougham K, Wu J, Battersby C, Gale C, Modi N, Angelini ED. Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning. Sci Rep 2021;11:7178. [PMID: 33785776 DOI: 10.1038/s41598-021-85878-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
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