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

Decision Support for Health Professionals (1646) Clinical Information and Decision Making (2081) Machine Learning (2244) Cardiac Disease Management (166) Heart Failure Self-Management (183) New Methods (1184) Cardiac Risk and Cardiac Risk Calculators (118) Computer-Aided Diagnosis (280)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58812, first published .
Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study

Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study

Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study

Authors of this article:

Xiangkui Jiang1 Author Orcid Image ;   Bingquan Wang1 Author Orcid Image
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  2. Parizad R, Hatwal J, Brar A, Desai R, Batta A, Mohan B. Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges. Vascular Health and Risk Management 2026;Volume 22:1 View

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