Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data.

Journal: Journal of cardiovascular pharmacology and therapeutics

Published Date: Mar 8, 2021

Abstract

BACKGROUND: Drug-induced QT prolongation is a potentially preventable cause of morbidity and mortality, however there are no widespread clinical tools utilized to predict which individuals are at greatest risk. Machine learning (ML) algorithms may provide a method for identifying these individuals, and could be automated to directly alert providers in real time.

Authors

Steven T Simon

Division of Cardiology, 12225University of Colorado School of Medicine, Aurora, CO, USA.
Divneet Mandair

Division of Internal Medicine, University of Colorado School of Medicine, Aurora, CO, USA.
Premanand Tiwari

Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora.
Michael A Rosenberg

Division of Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America.

Keywords

Adult Aged Deep Learning Electrocardiography Electronic Health Records Female Humans Long QT Syndrome Machine Learning Male Middle Aged Risk Assessment

External Resources

View on PubMed Access via DOI PubMed (33682475)

Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data.

Abstract

Authors

Keywords

External Resources

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