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

Machine Learning (2316) Clinical Information and Decision Making (2157) Big Data (320) Decision Support for Health Professionals (1705) Nursing (145) Electronic Health Records (1425) Advanced Data Analytics in eHealth (250)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56671, first published .
Two female doctors in white coats walk down a hospital hallway.

Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study

Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study

Authors of this article:

Eui Geum Oh1, 2, 3 Author Orcid Image ;   Sunyoung Oh4 Author Orcid Image ;   Seunghyeon Cho5 Author Orcid Image ;   Mir Moon3, 6, 7 Author Orcid Image
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  2. Mao Y, Chen Y, Zhai M, Jin P, Li W, Gou Y, Liu Y, Zhang J, Yang J. Periprocedural evaluation of patients with BAV stenosis undergoing TAVR: a machine learning-based study. Open Heart 2026;13(1):e003749 View

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  1. M.Ayyadurai , Anand M, P E. 2025 5th International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN). Cross-Silo Prediction of Hospital Readmissions using a Federated Learning Framework View
  2. Sushil A, Alamgir S, Rahman H, Gupta P, Sushil Pranta N, Chowdhury Z, Mawa J, Resat N, Uddin A. 2025 IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON). Balancing Accuracy and Interpretability: Machine Learning Approaches to Predict Heart Failure Readmissions View