• XGBoost machine learning algor...

Treffer: XGBoost machine learning algorithm for predicting unplanned readmission in elderly patients with coronary heart disease.

Title:
XGBoost machine learning algorithm for predicting unplanned readmission in elderly patients with coronary heart disease.
Authors:
Song X; Department of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, China., Shi J; Department of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, China., Zhu C; Department of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, China., Xian F; Department of Oncology, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, 637000, China., Dong Z; Department of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, China., Li J; Department of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, China. Electronic address: 1371117044@qq.com.
Source:
Geriatric nursing (New York, N.Y.) [Geriatr Nurs] 2025 Nov-Dec; Vol. 66 (Pt B), pp. 103609. Date of Electronic Publication: 2025 Sep 12.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Mosby-Yearbook Country of Publication: United States NLM ID: 8309633 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1528-3984 (Electronic) Linking ISSN: 01974572 NLM ISO Abbreviation: Geriatr Nurs Subsets: MEDLINE
Imprint Name(s):
Publication: St Louis Mo : Mosby-Yearbook
Original Publication: [New York : American Journal of Nursing Co.,
MeSH Terms:
Patient Readmission*/statistics & numerical data , Coronary Disease*/therapy , Machine Learning* , Algorithms*, Humans ; Male ; Aged ; Female ; Retrospective Studies ; Aged, 80 and over ; China ; Length of Stay/statistics & numerical data ; ROC Curve ; Boosting Machine Learning Algorithms
Contributed Indexing:
Keywords: Coronary heart disease; Elderly; Machine learning; Unplanned readmission
Entry Date(s):
Date Created: 20250913 Date Completed: 20251207 Latest Revision: 20251207
Update Code:
20251208
DOI:
10.1016/j.gerinurse.2025.103609
PMID:
40945246
Database:
MEDLINE

Weitere Informationen

Background: Most studies have focused on 30-day rather than 1-year unplanned readmissions in elderly patients with coronary heart disease (CHD). The extreme gradient boosting (XGBoost)-based model demonstrates good predictive performance and explainability.
Objective: This study aimed to establish an XGBoost model to predict 1-year unplanned readmission in Chinese elderly CHD patients.
Methods: The clinical data of elderly CHD patients were collected retrospectively. The stepwise forward method was used for feature selection. The area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) and calibration curve were used to evaluate the performance of the ML models. SHapley Additive exPlanations (SHAP) analysis was used to evaluate the importance of features.
Results: A total of 2137 patients were enrolled. The AUROC of the XGBoost model was 0.704, and the AUPRC was 0.392. SHAP analysis showed that length of stay (LOS), age-adjusted Charlson comorbidity index (ACCI), monocyte count, blood glucose level and red blood cell (RBC) count were the most important predictors.
Conclusion: XGBoost can predict 1-year unplanned readmissions in elderly patients with CHD and identify the risk factors.
(Copyright © 2025 The Author(s). Published by Elsevier Inc. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.