ArticleInternational journal of cardiology. Heart & vasculature2021
Predicting mortality and hospitalization in heart failure using machine learning: A systematic literature review.
Article in International journal of cardiology. Heart & vasculature, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 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
- Prognostic models in populations with heart failure: a systematic review and meta-analysis.Systematic reviews · 2026Pooled it
- Development and validation of a machine learning-based clinical decision support tool for stratifying intravenous medication risk in hospitalized patients with heart failure.International journal of clinical pharmacy · 2026Article
- Machine Learning Prediction of Heart Failure Readmissions: Insights From a Multicenter Emergency Department Trial.JACC. Advances · 2026Article
- Enhanced Prediction of Cardiovascular Disease Through Integrated Machine Learning Models Combining Clinical and Demographic Characteristics.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.ESC heart failure · 2024Review
- Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol.BMJ open · 2024Article
- Feature elimination and stacking framework for accurate heart disease detection in IoT healthcare systems using clinical data.Frontiers in medicine · 2024Article
- Machine Learning Models for Prediction of Sex Based on Lumbar Vertebral Morphometry.Diagnostics (Basel, Switzerland) · 2023Article
- Developing Clinical Risk Prediction Models for Worsening Heart Failure Events and Death by Left Ventricular Ejection Fraction.Journal of the American Heart Association · 2023Article
- Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure.Bioengineering (Basel, Switzerland) · 2023Article
- Machine learning applied to electronic health record data in home healthcare: A scoping review.International journal of medical informatics · 2023Article
- Article
- Nuclear Cardiology Data Analyzed Using Machine Learning.Annals of nuclear cardiology · 2022Review
- A review of risk concepts and models for predicting the risk of primary stroke.Frontiers in neuroinformatics · 2022Review
- Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe partnership between humans and machines can enhance clinical decisions accuracy, leading to improved patient outcomes. Despite this, the application of machine learning techniques in the healthcare sector, particularly in guiding heart failure patient management, remains unpopular. This systematic review aims to identify factors restricting the integration of machine learning derived risk scores into clinical practice when treating adults with acute and chronic heart failure.
methodsFour academic research databases and Google Scholar were searched to identify original research studies where heart failure patient data was used to build models predicting all-cause mortality, cardiac death, all-cause and heart failure-related hospitalization.
resultsThirty studies met the inclusion criteria. The selected studies' sample size ranged between 71 and 716 790 patients, and the median age was 72.1 (interquartile range: 61.1-76.8) years. The minimum and maximum area under the receiver operating characteristic curve (AUC) for models predicting mortality were 0.48 and 0.92, respectively. Models predicting hospitalization had an AUC of 0.47 to 0.84. Nineteen studies (63%) used logistic regression, 53% random forests, and 37% of studies used decision trees to build predictive models. None of the models were built or externally validated using data originating from Africa or the Middle-East.
conclusionsThe variation in the aetiologies of heart failure, limited access to structured health data, distrust in machine learning techniques among clinicians and the modest accuracy of existing predictive models are some of the factors precluding the widespread use of machine learning derived risk calculators.
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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.