Machine learning-based prediction model for 30-day readmission risk in elderly patients with type 2 diabetes mellitus and heart failure: a retrospective cohort study with SHAP interpretability analysis

This study developed a high-performing, interpretable machine learning model for predicting 30-day readmission risk, providing a valuable clinical decision-making tool.

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Publication Details

Journal
Frontiers in Cardiovascular Medicine
Published
2026-01-07
DOI
https://doi.org/10.3389/fcvm.2025.1673159
Citations
1
Primary Topic
Heart Failure Treatment and Management
Type
article
Field-Weighted Citation Impact
30.07
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article

Machine learning-based prediction model for 30-day readmission risk in elderly patients with type 2 diabetes mellitus and heart failure: a retrospective cohort study with SHAP interpretability analysis

Dasheng Lu, Y J Chen, L Wang, Mo Yang et al.
1 citations
Frontiers in Cardiovascular Medicine
Heart Failure Treatment and Management
30.07
article

Machine learning-based prediction model for 30-day readmission risk in elderly patients with type 2 diabetes mellitus and heart failure: a retrospective cohort study with SHAP interpretability analysis

Dasheng Lu, Y J Chen, L Wang, Mo Yang, Hong-Xiang Zhang, Chao Wei
article en
1 citations

Abstract

This study developed a high-performing, interpretable machine learning model for predicting 30-day readmission risk, providing a valuable clinical decision-making tool.

Frontiers in Cardiovascular MedicineVol. 12
Wannan Medical College (CN)
Good health and well-being
Openalex Percentile: Top 3%
Heart Failure Treatment and Management
30.07
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