ArticleJournal of the American Medical Informatics Association : JAMIA2021
A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence in hepatopancreatobiliary surgery for clinical outcome prediction: current perspective and future direction.Journal of robotic surgery · 2025Review
- Anthropometric, Nutritional, and Lifestyle Factors Involved in Predicting Food Addiction: An Agnostic Machine Learning Approach.Diseases (Basel, Switzerland) · 2025Article
- Machine Learning and Interpretability Study for Predicting 30-Day Unplanned Readmission Risk of Schizophrenia: A Retrospective Study.Neuropsychiatric disease and treatment · 2025Article
- Intelligent health in the IS area: A literature review and research agenda.Fundamental research · 2024Article
- A hybrid machine learning framework to improve prediction of all-cause rehospitalization among elderly patients in Hong Kong.BMC medical research methodology · 2023Article
- Machine Learning-Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal.JMIR medical informatics · 2022Review
- Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare.Patterns (New York, N.Y.) · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unplanned hospital readmissions are a burden to patients and increase healthcare costs. A wide variety of machine learning (ML) models have been suggested to predict unplanned hospital readmissions. These ML models were often specifically trained on patient populations with certain diseases. However, it is unclear whether these specialized ML models-trained on patient subpopulations with certain diseases or defined by other clinical characteristics-are more accurate than a general ML model trained on an unrestricted hospital cohort. In this study based on an electronic health record cohort of consecutive inpatient cases of a single tertiary care center, we demonstrate that accurate prediction of hospital readmissions may be obtained by general, disease-independent, ML models. This general approach may substantially decrease the cost of development and deployment of respective ML models in daily clinical routine, as all predictions are obtained by the use of a single model.
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Registered trials
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