ArticleESC heart failure2019
Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics.
Article in ESC heart failure, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 3 of them syntheses that pooled it.
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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
63 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.BMC medical informatics and decision making · 2026Pooled it
- Pooled it
- 2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2021Guideline
- Longitudinal trajectories of hospitalizations in US older adults with heart failure.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Machine Learning Prediction of Heart Failure Readmissions: Insights From a Multicenter Emergency Department Trial.JACC. Advances · 2026Article
- Establishment and validation of a machine learning-based predictive model for in-hospital mortality risk in acute myocardial infarction patients complicated with diabetes mellitus.Frontiers in cardiovascular medicine · 2026Article
- AI-based approach for heart failure readmission prediction using SCG, ECG, and GSR signals.Physiological measurement · 2025Article
- Evaluation of Machine Learning Model Performance in Diabetic Foot Ulcer: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Machine learning in predicting heart failure survival: a review of current models and future prospects.Heart failure reviews · 2025Review
- Machine learning-based model for worsening heart failure risk in Chinese chronic heart failure patients.ESC heart failure · 2025Article
- Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.ESC heart failure · 2024Article
- Maximizing Survival in Pediatric Congenital Cardiac Surgery Using Machine Learning, Explainability, and Simulation Techniques.Journal of clinical medicine · 2024Article
- Predictive Analytics in Heart Failure Risk, Readmission, and Mortality Prediction: A Review.Cureus · 2024Review
- Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.ESC heart failure · 2024Article
- Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention.Scientific reports · 2024Article
- Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach.Clinical cardiology · 2024Article
- A machine learning model to predict heart failure readmission: toward optimal feature set.Frontiers in artificial intelligence · 2024Article
- Machine learning based readmission and mortality prediction in heart failure patients.Scientific reports · 2023Article
- Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy.BMC cardiovascular disorders · 2023Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsMachine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results likely due to failure to take into account the class imbalance problem commonly seen with medical data. We developed a new ML approach to predict 30 day HF readmission or death and compared the performance of this model with other commonly used prediction models. METHODS AND
resultsWe identified all Western Australian patients aged above 65 years admitted for HF between 2003 and 2008 in the linked Hospital Morbidity Data Collection. Taking into consideration the class imbalance problem, we developed a multi-layer perceptron (MLP)-based approach to predict 30 day HF readmission or death and compared the predictive performances using the performance metrics, that is, area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPRC), sensitivity and specificity with other ML and regression models. Out of the 10 757 patients with HF, 23.6% were readmitted or died within 30 days of hospital discharge. We observed an AUC of 0.55, 0.53, 0.58, and 0.54 while an AUPRC of 0.39, 0.38, 0.46, and 0.38 for weighted random forest, weighted decision trees, logistic regression, and weighted support vector machines models, respectively. The MLP-based approach produced the highest AUC (0.62) and AUPRC (0.46) with 48% sensitivity and 70% specificity.
conclusionsWe show that for the medical data with class imbalance, the proposed MLP-based approach is superior to other ML and regression techniques for the prediction of 30 day HF readmission or death.
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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 OpenQuestion graph.