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

  • Authors
  • Alex Makhnevich
  • Douglas Barnaby
  • Eun Ji. Kim
  • Jamie S. Hirsch
  • Kevin Coppa
  • Marc D Paradis
  • Northwell COVID-19 Research Consortium
  • Saurav Chatterjee
  • Shubham Debnath
  • Stuart L. Cohen
  • Theodoros Zanos
  • Todd J. Levy
  • Viktor Tóth
  • Published
  • Bioelectronic Medicine

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.

  • Keywords
  • Artificial intelligence (AI)
  • Clinical decision-making
  • Coronavirus disease 19 (COVID-19)
  • Healthcare
  • Machine learning (ML)