ArticleCritical care explorations2021
A Machine Learning Classifier Improves Mortality Prediction Compared With Pediatric Logistic Organ Dysfunction-2 Score: Model Development and Validation.
Article in Critical care explorations, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Artificial Intelligence in Predicting Mortality Risk for Critically Ill Children in Pediatric Intensive Care Units: A Systematic Review.Health science reports · 2026Review
- Heart Rate Change as a Potential Digital Biomarker of Brain Death in Critically Ill Children With Acute Catastrophic Brain Injury.Critical care explorations · 2023Article
- Glycemic Stress Index: Does It Correlate with the Intensive Care Length of Stay?Children (Basel, Switzerland) · 2023Article
- Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery.Frontiers in pediatrics · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesTo determine whether machine learning algorithms can better predict PICU mortality than the Pediatric Logistic Organ Dysfunction-2 score.
designRetrospective study.
settingQuaternary care medical-surgical PICU. PATIENTS: All patients admitted to the PICU from 2013 to 2019.
interventionsNone. MEASUREMENTS AND MAIN
resultsWe investigated the performance of various machine learning algorithms using the same variables used to calculate the Pediatric Logistic Organ Dysfunction-2 score to predict PICU mortality. We used 10,194 patient records from 2013 to 2017 for training and 4,043 patient records from 2018 to 2019 as a holdout validation cohort. Mortality rate was 3.0% in the training cohort and 3.4% in the validation cohort. The best performing algorithm was a random forest model (area under the receiver operating characteristic curve, 0.867 [95% CI, 0.863-0.895]; area under the precision-recall curve, 0.327 [95% CI, 0.246-0.414]; F1, 0.396 [95% CI, 0.321-0.468]) and significantly outperformed the Pediatric Logistic Organ Dysfunction-2 score (area under the receiver operating characteristic curve, 0.761 [95% CI, 0.713-0.810]; area under the precision-recall curve (0.239 [95% CI, 0.165-0.316]; F1, 0.284 [95% CI, 0.209-0.360]), although this difference was reduced after retraining the Pediatric Logistic Organ Dysfunction-2 logistic regression model at the study institution. The random forest model also showed better calibration than the Pediatric Logistic Organ Dysfunction-2 score, and calibration of the random forest model remained superior to the retrained Pediatric Logistic Organ Dysfunction-2 model.
conclusionsA machine learning model achieved better performance than a logistic regression-based score for predicting ICU mortality. Better estimation of mortality risk can improve our ability to adjust for severity of illness in future studies, although external validation is required before this method can be widely deployed.
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