This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2226) Heart Failure Self-Management (139) Machine Learning (1401) Telehealth and Telemonitoring (1575) e-Cardiology and Telemonitoring in Cardiology (150) Decision Support for Health Professionals (1147) Clinical Informatics (919)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41775, first published .
Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study

Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study

Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study

Authors of this article:

Boshu Ru1 Author Orcid Image ;   Xi Tan1 Author Orcid Image ;   Yu Liu1 Author Orcid Image ;   Kartik Kannapur2 Author Orcid Image ;   Dheepan Ramanan2 Author Orcid Image ;   Garin Kessler2, 3 Author Orcid Image ;   Dominik Lautsch1 Author Orcid Image ;   Gregg Fonarow4 Author Orcid Image
Article Authors Cited by (7) Tweetations (5) Metrics

Boshu Ru   1 , PhD ;   Xi Tan   1 , PhD, PharmD ;   Yu Liu   1 , MS ;   Kartik Kannapur   2 , MS ;   Dheepan Ramanan   2 , BA ;   Garin Kessler   2, 3 , BA ;   Dominik Lautsch   1 , PhD ;   Gregg Fonarow   4 , MD

1 Merck & Co, Inc, Rahway, NJ, United States

2 Amazon Web Services Inc, Seattle, WA, United States

3 School of Continuing Studies, Georgetown University, Washington, DC, United States

4 Ahmanson-UCLA Cardiomyopathy Center, University of California, Los Angeles, Los Angeles, CA, United States

Corresponding Author:

  • Gregg Fonarow, MD
  • Ahmanson-UCLA Cardiomyopathy Center
  • University of California, Los Angeles
  • 10833 LeConte Avenue
  • Los Angeles, CA, 90095
  • United States
  • Phone: 1 310-206-9112
  • Email: GFonarow@mednet.ucla.edu