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

Heart Failure Self-Management (150) Clinical Information and Decision Making (1407) Machine Learning (1465) Decision Support for Health Professionals (1185) Cardiac Disease Management (94) AI Language Models in Health Care (58) Secondary Use of Clinical Data for Research and Surveillance (366) Data Science (270)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54994, first published .
Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study

Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study

Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study

Authors of this article:

Rebecca T Levinson1 Author Orcid Image ;   Cinara Paul1 Author Orcid Image ;   Andreas D Meid2 Author Orcid Image ;   Jobst-Hendrik Schultz1 Author Orcid Image ;   Beate Wild1 Author Orcid Image
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Rebecca T Levinson   1 * , PhD ;   Cinara Paul   1 * , Dr med ;   Andreas D Meid   2 , Dr sc hum ;   Jobst-Hendrik Schultz   1 , Dr med ;   Beate Wild   1 , Dr sc hum

1 Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany

2 Medical Faculty of Heidelberg, Internal Medicine IX - Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany

*these authors contributed equally

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