ArticleInternational journal of environmental research and public health2023
Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.
Article in International journal of environmental research and public health, 2023. 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.
- Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.Pharmacology research & perspectives · 2026Article
- Predictive models for ICU patient readmission based on machine learning: A systematic review.Journal of the Intensive Care Society · 2026Article
- Mitigating Clinical Confounding in AI Models: A Comparative Analysis of Associative and Causal-Fused AI for ICU Readmissions.Healthcare (Basel, Switzerland) · 2026Article
- Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records.PloS one · 2025Article
- Transforming critical care: the digital revolution's impact on intensive care units.Frontiers in digital health · 2025Review
- Validation of the inadequate delivery of oxygen index in an adult cardiovascular intensive care unit.JTCVS open · 2024Article
- PSO-XnB: a proposed model for predicting hospital stay of CAD patients.Frontiers in artificial intelligence · 2024Article
- Artificial intelligence to predict bed bath time in Intensive Care Units.Revista brasileira de enfermagem · 2024Article
- Evaluation and Treatment of Obesity and Its Comorbidities: 2022 Update of Clinical Practice Guidelines for Obesity by the Korean Society for the Study of Obesity.Journal of obesity & metabolic syndrome · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today's hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the ICU in the same hospital stay have a higher risk of mortality, morbidity, longer length of stay, and increased cost. The methodology proposed to predict ICU readmission could improve the patients' care. The objective of this work is to explore and evaluate the potential improvement of existing models for predicting early ICU patient readmission by using optimized artificial intelligence algorithms and explainability techniques. In this work, XGBoost is used as a predictor model, combined with Bayesian techniques to optimize it. The results obtained predicted early ICU readmission (AUROC of 0.92 ± 0.03) improves state-of-the-art consulted works (whose AUROC oscillate between 0.66 and 0.78). Moreover, we explain the internal functioning of the model by using Shapley Additive Explanation-based techniques, allowing us to understand the model internal performance and to obtain useful information, as patient-specific information, the thresholds from which a feature begins to be critical for a certain group of patients, and the feature importance ranking.
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