ArticleJournal of clinical medicine2019
Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 of them syntheses that pooled it.
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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.
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Who cites it
24 citing papers in PubMed, 4 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
- Pooled it
- Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review.Medicina (Kaunas, Lithuania) · 2021Pooled it
- Predictive Performance of Machine Learning Models for Heart Failure Readmission: A Systematic Review.Biomedicines · 2025Review
- Predictive Analytics in Heart Failure Risk, Readmission, and Mortality Prediction: A Review.Cureus · 2024Review
- Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis.Frontiers in cardiovascular medicine · 2024Article
- A comprehensive secure system enabling healthcare 5.0 using federated learning, intrusion detection and blockchain.PeerJ. Computer science · 2024Article
- Machine Learning Models for Prediction of Sex Based on Lumbar Vertebral Morphometry.Diagnostics (Basel, Switzerland) · 2023Article
- Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning.BMC medical informatics and decision making · 2023Article
- Machine learning-based risk prediction model for canine myxomatous mitral valve disease using electronic health record data.Frontiers in veterinary science · 2023Article
- Short-term anti-remodeling effects of gliflozins in diabetic patients with heart failure and reduced ejection fraction: an explainable artificial intelligence approach.Frontiers in pharmacology · 2023Article
- Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning.Machine learning in medical imaging. MLMI (Workshop) · 2022Article
- Supervised Pretraining through Contrastive Categorical Positive Samplings to Improve COVID-19 Mortality Prediction.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2022Article
- The path from big data analytics capabilities to value in hospitals: a scoping review.BMC health services research · 2022Article
- Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis.Computational intelligence and neuroscience · 2022Article
- Predicting the behavioral intentions of hospice and palliative care providers from real-world data using supervised learning: A cross-sectional survey study.Frontiers in public health · 2022Article
- An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques.Journal of clinical medicine · 2021Article
- Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods.Healthcare informatics research · 2021Article
- Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.
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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.