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

Heart Failure Self-Management (179) Clinical Information and Decision Making (1927) Machine Learning (2083) Decision Support for Health Professionals (1548) Cardiac Disease Management (158) AI Language Models in Health Care (206) Secondary Use of Clinical Data for Research and Surveillance (475) Data Science (358)

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
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. MWR -. Stellungnahme „Künstliche Intelligenz in der Medizin“. Deutsches Ärzteblatt Online 2025 View
  2. Tawfeek M, Alrashdi I, Alruwaili M, Allahem H, Pedrammehr S. Cardiovascular disease detection: A hybrid machine learning-AI framework for personalized diagnosis and risk assessment. PLOS One 2025;20(10):e0335421 View

Conference Proceedings

  1. Sriram S, Kumar V, Jayakrishnan G, Vijayaraj N. 2025 International Conference on Networks and Cryptology (NETCRYPT). Leveraging Advanced Preprocessing and CNN for Mortality and Rehospitalization Prediction in Heart Failure Patients View