SynthesisCirculation. Cardiovascular quality and outcomes2016
Analysis of Machine Learning Techniques for Heart Failure Readmissions.
Synthesis in Circulation. Cardiovascular quality and outcomes, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03804606 (Risk and Benefit Informed MTM Pharmacist Intervention in Heart Failure), which is not on this map. Cited by 175 papers, 6 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Risk and Benefit Informed MTM Pharmacist Intervention in Heart Failure
Who cites it
175 citing papers in PubMed, 6 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
- Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis.Computational intelligence and neuroscience · 2022Pooled it
- Pooled it
- Guideline of the Brazilian Society of Cardiology on Telemedicine in Cardiology - 2019.Arquivos brasileiros de cardiologia · 2019Guideline
- Outpatient Worsening Heart Failure as a Target for Therapy: A Review.JAMA cardiology · 2018Pooled it
- Longitudinal trajectories of hospitalizations in US older adults with heart failure.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Multichamber Myocardial Strain for Cardiovascular Risk Stratification in Liver Transplant Recipients Using Interpretable Machine Learning.JACC. Advances · 2026Article
- Deep Learning-Based Prediction of Heart Failure Readmission Using Longitudinal Cardiac Device Data.Journal of cardiovascular electrophysiology · 2026Article
- Application Progress of Machine Learning in Prognostic Prediction of Percutaneous Coronary Intervention: A Systematic Review.Journal of clinical medicine research · 2026Review
- Machine Learning Prediction of Heart Failure Readmissions: Insights From a Multicenter Emergency Department Trial.JACC. Advances · 2026Article
- Prediction of Risk of Unplanned Readmission or Death in Elderly Heart Failure Patients During the Vulnerable Phase: Machine Learning With SHAP Interpretation.FASEB bioAdvances · 2026Article
- Comparative accuracy of a predictive algorithm versus cardiologist assessment for 90-day readmission in decompensated chronic heart failure.BMC cardiovascular disorders · 2026Observational
- A narrative review on the use of artificial intelligence in cardiovascular medicine.Cardiovascular diagnosis and therapy · 2026Review
- Beyond Composite Indices: Comprehensive Social Determinants Improve Heart Failure Readmission Prediction.Journal of the American Heart Association · 2026Article
- Explainable machine learning for long-term cardiovascular disease risk prediction in Chinese middle-aged and older adults: a 9-year longitudinal cohort study with web-based risk calculator.Scientific reports · 2026Article
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Risk Prediction of Chronic Kidney Disease Progression in Type 2 Diabetes Mellitus Across Diverse Populations.NPJ digital medicine · 2026Article
- Risk Stratification and Outcome Prediction in Heart Failure Patients With Cardiac Implantable Electronic Devices Using Machine Learning Analysis From the HINODE Study.Circulation reports · 2026Article
- Machine learning-based prediction of in-hospital mortality in patients with decompensated heart failure using the Charlson Comorbidity Index.SAGE open medicine · 2026Article
115 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundThe current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. METHODS AND
resultsUsing data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively).
conclusionsMachine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates.
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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 OpenQuestion graph.