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10.1186/s42234-020-00050-8

http://scihub22266oqcxt.onion/10.1186/s42234-020-00050-8
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32665967!7347420!32665967
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suck abstract from ncbi


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pmid32665967      Bioelectron+Med 2020 ; 6 (ä): 14
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  • Machine learning to assist clinical decision-making during the COVID-19 pandemic #MMPMID32665967
  • Debnath S; Barnaby DP; Coppa K; Makhnevich A; Kim EJ; Chatterjee S; Toth V; Levy TJ; Paradis MD; Cohen SL; Hirsch JS; Zanos TP
  • Bioelectron Med 2020[]; 6 (ä): 14 PMID32665967show ga
  • BACKGROUND: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. MAIN BODY: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. CONCLUSION: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.
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