Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19.

Journal: Journal of critical care

Published Date: Apr 1, 2021

Abstract

PURPOSE: The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19.

Authors

Varun Arvind

Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Jun S Kim

Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Brian H Cho

Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Eric Geng

Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America.
Samuel K Cho

Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Keywords

Adult Aged Aged, 80 and over Algorithms Area Under Curve Cohort Studies COVID-19 Female Hospitalization Humans Intubation, Intratracheal Male Middle Aged New York City Predictive Value of Tests Prognosis Respiration, Artificial Retrospective Studies SARS-CoV-2 Supervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (33238219)

Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19.

Abstract

Authors

Keywords

External Resources

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