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

Debnath, Shubham; Barnaby, Douglas P.; Coppa, Kevin; Makhnevich, Alexander; Kim, Eun Ji; Chatterjee, Saurav; Tóth, Viktor; Levy, Todd J.; Paradis, Marc d.; Cohen, Stuart L.; Hirsch, Jamie S.; Zanos, Theodoros P.

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.