Publication Date
2020
Journal Title
Bioelectron Med
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.
Volume Number
6
Pages
14
Document Type
Article
Status
Faculty, Northwell Researcher, Northwell Resident
Facility
School of Medicine; Northwell Health
Primary Department
Emergency Medicine
Additional Departments
Cardiology; General Internal Medicine; Molecular Medicine; Radiology; COVID-19 Publications
PMID
DOI
10.1186/s42234-020-00050-8