ArticleBMC medical informatics and decision making2024
Early prediction of mortality upon intensive care unit admission.
Article in BMC medical informatics and decision making, 2024. 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.
- A multicenter machine learning model for predicting ICU mortality in mechanically ventilated patients: development and external validation.BMC medical informatics and decision making · 2026Article
- Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study.Intensive care medicine experimental · 2026Article
- Thiamine use and short-term prognosis in critical cerebrovascular disease: a propensity score matched retrospective analysis from the MIMIC-IV database.BMC neurology · 2025Observational
- CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction.BMC medical informatics and decision making · 2025Article
- AI prediction of extubation success within a novel three-stage liberation framework: development, validation, and implementation of the Stage-3 model.Frontiers in medicine · 2025Article
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Authors and funding
15 authors.
Funding
Abstract
backgroundWe aimed to develop and validate models for predicting intensive care unit (ICU) mortality of critically ill adult patients as early as upon ICU admission.
methodsCombined data of 79,657 admissions from two teaching hospitals' ICU databases were used to train and validate the machine learning models to predict ICU mortality upon ICU admission and at 24 h after ICU admission by using logistic regression, gradient boosted trees (GBT), and deep learning algorithms.
resultsIn the testing dataset for the admission models, the ICU mortality rate was 7%, and 38.4% of patients were discharged alive or dead within 1 day of ICU admission. The area under the receiver operating characteristic curve (0.856, 95% CI 0.845-0.867) and area under the precision-recall curve (0.331, 95% CI 0.323-0.339) were the highest for the admission GBT model. The ICU mortality rate was 17.4% in the 24-hour testing dataset, and the performance was the highest for the 24-hour GBT model.
conclusionThe ADM models can provide crucial information on ICU mortality as early as upon ICU admission. 24 H models can be used to improve the prediction of ICU mortality for patients discharged more than 1 day after ICU admission.
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