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Analysis of Machine Learning Techniques for Heart Failure Readmissions
Open accessAbstract
Background—: The 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 Results—: Using 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). Conclusions—: Machine 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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“Applied to electronic patient records, AI has been used to predict the risk of cardiovascular disease, for instance acute coronary syndrome (22) and heart failure (23) better than traditional scales.”
From Artificial Intelligence in Medicine: Today and Tomorrow (2020) · cites this paper as [23]
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Metadata
- Authors
- Mortazavi, B. J., Downing, N. S., Bucholz, E. M., Dharmarajan, K., Manhapra, A., Li, S.-X., Negahban, S. N., & Krumholz, H. M.
- Affiliations
- Yale New Haven Hospital (US)
- Venue
- Circulation: Cardiovascular Quality and Outcomes
- Published
- 2016-11
- Volume
- 9
- Issue
- 6
- Pages
- 629-640
- ISSN
- 1941-7705 (ISSN-L)
- 1941-7713
- PMC
- PMC5459389
- OpenAlex
- W2549885908
- Cited by
- 401
- References
- 46
- Citations by year
- 2026: 30 · 2025: 38 · 2024: 46 · 2023: 48 · 2022: 41 · 2021: 68 · 2020: 54 · 2019: 44 · 2018: 23 · 2017: 7 · 2016: 2
- FWCI
- 43.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Circulation Cardiovascular Quality and Outcomes (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- American Federation for Aging Research
- Yale University
- National Institute on Aging (K23AG048331)
- National Heart, Lung, and Blood Institute (U01HL105270, R01HL080228)
- National Institute on Drug Abuse (R25DA033211)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- heart failure, hospital readmission prediction, machine learning, random forest, boosting, support vector machine, Poisson regression, logistic regression, all-cause readmission, heart failure readmission, remote patient monitoring, risk prediction
- MeSH terms
- Aged, Algorithms, Female, Heart Failure, Humans, Male, Middle Aged, Patient Readmission, Risk Factors, Time Factors, Reproducibility of Results, Logistic Models, Randomized Controlled Trials as Topic, Databases, Factual, Telemedicine, Nonlinear Dynamics, Risk Assessment, Data Mining, Support Vector Machine
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