ArticleJournal of clinical medicine2023
Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- Timing-specific factors associated with early, intermediate, and late readmission after heart failure hospitalization.ESC heart failure · 2026Article
- Deep Learning-Based Prediction of Heart Failure Readmission Using Longitudinal Cardiac Device Data.Journal of cardiovascular electrophysiology · 2026Article
- Machine learning for prediction of in-ICU mortality in sepsis: an observational study using the MIMIC IV database.Archives of medical science : AMS · 2026Article
- Observational
- Machine Learning-Driven Prediction of One-Year Readmission in HFrEF Patients: The Key Role of Inflammation.Clinical interventions in aging · 2025Article
- Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study.JMIR medical informatics · 2024Article
- Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.ESC heart failure · 2024Article
- SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation.Scientific reports · 2024Article
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- Analysis of the Larissa Heart Failure Risk Score: Predictive Value in 9207 Patients Hospitalized for Heart Failure from a Single Center.Journal of personalized medicine · 2023Article
- Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599-0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.