ArticleCritical care (London, England)2024
Real-time machine learning model to predict short-term mortality in critically ill patients: development and international validation.
Article in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- STREAM: A data-driven framework for physiological state monitoring in ICU patients.PLOS digital health · 2026Article
- Generalization, Cross-ICU Transfer, and Explainability of a Mortality and Time-to-Discharge Framework for the Intensive Care Unit.Journal of clinical medicine · 2026Article
- Value of AI in Critical Care Using Real-World Evidence on Intensive Care Unit Mortality Prediction: Cost-Utility Analysis.Journal of medical Internet research · 2026Article
- A Data-Driven Medication Regimen Complexity Score for Critically Ill Patients: MRC-ICU 2.0.Pharmacotherapy · 2026Observational
- Machine Learning for Risk Stratification in Acute Pancreatitis: Current Evidence and Future Perspectives.Journal of clinical medicine · 2026Review
- A multicenter machine learning model for predicting ICU mortality in mechanically ventilated patients: development and external validation.BMC medical informatics and decision making · 2026Article
- First-Day Glycemic Exposure and 28-Day Mortality in the ICU: A Multicohort Study.Research square · 2026Article
- Prediction of early death in peripheral T-cell lymphoma-NOS patients based on machine learning.Annals of hematology · 2026Article
- Review
- Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study.BMC medical informatics and decision making · 2026Article
- Development and Validation of a Multivariable Machine Learning Model for Mortality Prediction among Intensive Care Unit Patients.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Real-time monitoring of in-hospital mortality risk in intensive care units heart failure patients using an extreme gradient boosting model.Journal of geriatric cardiology : JGC · 2026Article
- First-Day Glycemic Exposure and 28-Day Mortality in the ICU: A Multicenter Cohort Study.Research square · 2026Article
- A Framework and Method for Measuring the Implementation of Data Science in Critical Care.Critical care explorations · 2026Article
- Article
- Artificial intelligence-driven clustering for phenotyping life-threatening prehospital trauma.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Article
- Early prediction of in-hospital deterioration after emergency department admission using machine learning models.BMC emergency medicine · 2026Article
- Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction.NPJ digital medicine · 2026Article
- Development and external validation of an interpretable machine learning model for early prediction of in-hospital mortality in neurocritical care.Frontiers in neurology · 2026Article
- Dynamic prediction of the development of extrahepatic organ failures in ICU patients with decompensated cirrhosis.eGastroenterology · 2026Article
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Authors and funding
6 authors.
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Abstract
backgroundA real-time model for predicting short-term mortality in critically ill patients is needed to identify patients at imminent risk. However, the performance of the model needs to be validated in various clinical settings and ethnicities before its clinical application. In this study, we aim to develop an ensemble machine learning model using routinely measured clinical variables at a single academic institution in South Korea.
methodsWe developed an ensemble model using deep learning and light gradient boosting machine models. Internal validation was performed using the last two years of the internal cohort dataset, collected from a single academic hospital in South Korea between 2007 and 2021. External validation was performed using the full Medical Information Mart for Intensive Care (MIMIC), eICU-Collaborative Research Database (eICU-CRD), and Amsterdam University Medical Center database (AmsterdamUMCdb) data. The area under the receiver operating characteristic curve (AUROC) was calculated and compared to that for the National Early Warning Score (NEWS).
resultsThe developed model (iMORS) demonstrated high predictive performance with an internal AUROC of 0.964 (95% confidence interval [CI] 0.963-0.965) and external AUROCs of 0.890 (95% CI 0.889-0.891) for MIMIC, 0.886 (95% CI 0.885-0.887) for eICU-CRD, and 0.870 (95% CI 0.868-0.873) for AmsterdamUMCdb. The model outperformed the NEWS with higher AUROCs in the internal and external validation (0.866 for the internal, 0.746 for MIMIC, 0.798 for eICU-CRD, and 0.819 for AmsterdamUMCdb; p < 0.001).
conclusionsOur real-time machine learning model to predict short-term mortality in critically ill patients showed excellent performance in both internal and external validations. This model could be a useful decision-support tool in the intensive care units to assist clinicians.
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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.