Machine learning to assist clinical decision-making during the COVID-19 pandemic

Bioelectronic Medicine

the Northwell COVID-19 Research Consortium¹  Saurav Chatterjee²  Kevin Coppa³  Jamie S. Hirsch⁴  Alexander Makhnevich⁴  Marc d. Paradis⁵  Shubham Debnath⁶  Todd J. Levy⁶  Viktor Tóth⁶  Theodoros P. Zanos⁶  Douglas P. Barnaby⁷  Stuart L. Cohen⁷  Eun Ji Kim⁷ 

[1] ;Cardiology, Long Island Jewish Medical Center and Feinstein Institutes for Medical Research, Northwell Health;Department of Information Services, Northwell Health;Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health;Holdings and Ventures, Northwell Health;Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell Health;Institute of Health Innovations and Outcomes Research, Feinstein Institutes for Medical Research, Northwell Health;

关键词: Artificial intelligence (AI);    Clinical decision-making;    Coronavirus disease 19 (COVID-19);    Healthcare;    Machine learning (ML);   

DOI  :  10.1186/s42234-020-00050-8

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown