ArticleArabian journal for science and engineering2021
Current Trends in Readmission Prediction: An Overview of Approaches.
Article in Arabian journal for science and engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Risk Prediction Models for Hospital Readmission After Percutaneous Coronary Intervention: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2025Pooled it
- Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PloS one · 2024Pooled it
- Predicting 30-Day Readmission After Stroke: A Systematic Review and Meta-Analysis to Inform Predictor Selection.Diagnostics (Basel, Switzerland) · 2026Review
- Risk factors for frequent unplanned hospital visits in older multimorbid patients after an emergency department visit: a retrospective cohort study.Aging clinical and experimental research · 2025Article
- Predicting 28-day all-cause unplanned hospital re-admission of patients with alcohol use disorders: a machine learning approach.Alcohol and alcoholism (Oxford, Oxfordshire) · 2025Article
- Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records.PloS one · 2025Article
- Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment.JMIR research protocols · 2024Article
- BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study.Journal of medical Internet research · 2023Article
- Identifying patients at risk of unplanned re-hospitalisation using statewide electronic health records.Scientific reports · 2022Article
- Impact of social determinants of health on improving the LACE index for 30-day unplanned readmission prediction.JAMIA open · 2022Article
- A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.
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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.