ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2023
Building Prediction Models for 30-Day Readmissions Among ICU Patients Using Both Structured and Unstructured Data in Electronic Health Records.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Predictive models for ICU patient readmission based on machine learning: A systematic review.Journal of the Intensive Care Society · 2026Article
- Article
- Deep Learning for Early Prediction of Adverse Events in Intensive Care Units Using Electronic Health Records: A Methodological Review.Risk management and healthcare policy · 2026Review
- Factors influencing unplanned readmissions after paediatric haematopoietic stem cell transplantation and effect on overall survival.Scientific reports · 2025Article
- Transformer-based time-to-event prediction for chronic kidney disease deterioration.Journal of the American Medical Informatics Association : JAMIA · 2024Article
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2 authors.
Funding
Abstract
ICU readmissions are associated with poor outcomes for patients and poor performance of hospitals. Patients who are readmitted have an increased risk of in-hospital deaths; hospitals with a higher read-mission rate have a reduced profitability, due to an increase in cost and reduced payments from Medicare and Medicaid programs. Predicting a patient's likelihood of being readmitted to the ICU can help reduce early discharges, the risk of in-hospital deaths, and help in-crease profitability. In this study, we built and evaluated multiple machine learning models to predict 30-day readmission rates of ICU patients in the MIMIC-III database. We used both the structured data including demographics, laboratory tests, comorbidities, and unstructured discharge summaries as the predictors and evaluated different combinations of features. The best performing model in this study Logistic Regression achieved an AUROC of 75.7%. This study shows the potential of leveraging machine learning and deep learning for predicting ICU readmissions.
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