ArticleHealthcare (Basel, Switzerland)2022
Forecasting Hospital Readmissions with Machine Learning.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Machine Learning Prediction of One-Year Rehospitalization in Dementia: A Retrospective Cohort Study.Health science reports · 2026Article
- Hospital readmission after an acute admission to internal medicine: Causes and risk factors in a tertiary care center in Saudi Arabia.Saudi medical journal · 2025Article
- Using Machine Learning to Improve Readmission Risk in Surgical Patients in South Africa.International journal of environmental research and public health · 2025Article
- Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models.Diagnostics (Basel, Switzerland) · 2024Article
- Machine Learning-Based Prediction of Readmission Risk in Cardiovascular and Cerebrovascular Conditions Using Patient EMR Data.Healthcare (Basel, Switzerland) · 2024Article
- Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda.Journal of personalized medicine · 2023Article
- Predicting 30-day readmissions in pneumonia patients using machine learning and residential greenness.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients' readmissions allows for timely intervention and better post-discharge strategies, preventing future life-threatening events, and reducing medical costs to either the patient or the healthcare system. In this paper, four machine learning models are used to forecast readmissions: support vector machines with a linear kernel, support vector machines with an RBF kernel, balanced random forests, and weighted random forests. The dataset consists of 11,172 actual records of hospitalizations obtained from the General Hospital of Komotini "Sismanogleio" with a total of 24 independent variables. Each record is composed of administrative, medical-clinical, and operational variables. The experimental results indicate that the balanced random forest model outperforms the competition, reaching a sensitivity of 0.70 and an AUC value of 0.78.
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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.