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

Decision Support for Health Professionals (1222) Clinical Information and Decision Making (1454) Machine Learning (1521) Cardiac Disease Management (103) Heart Failure Self-Management (158) New Methods (970) Cardiac Risk and Cardiac Risk Calculators (50) Computer-Aided Diagnosis (140)

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
Article Authors Cited by Tweetations Metrics

Xiangkui Jiang   1 * , PhD ;   Bingquan Wang   1 * , MEng

1 School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China

*all authors contributed equally

Corresponding Author:

  • Xiangkui Jiang, PhD
  • School of Automation
  • Xi’an University of Posts and Telecommunications
  • No. 563 Chang'an South Road, Yanta District
  • Xi’an, Shaanxi, 710121
  • China
  • Phone: 86 17810791125
  • Email: jiangxiangkui@xupt.edu.cn