ArticleScientific reports2021
Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in pediatric intensive care units: current applications in sepsis management.World journal of pediatrics : WJP · 2026Pooled it
- Red blood cell distribution width as a predictor of mortality in critically ill pediatric patients: a systematic review and meta-analysis.Frontiers in pediatrics · 2025Pooled it
- Machine Learning Using Electroencephalography Predicts Acute Cerebral Injury in the Pediatric ICU.Neurocritical care · 2026Article
- Application of Machine Learning Algorithms for Predicting Infant Mortality in India: An Analysis of the National Family Health Survey-5, 2019-2021.Healthcare informatics research · 2026Article
- Artificial Intelligence in Predicting Mortality Risk for Critically Ill Children in Pediatric Intensive Care Units: A Systematic Review.Health science reports · 2026Review
- Personalized decision-making through AI solutions in pediatric emergency medicine: Focusing on febrile children.PLOS digital health · 2025Review
- Prediction cardiovascular deterioration in a paediatric intensive care unit (PicEWS): a machine learning modelling study of routinely collected health-care data.EClinicalMedicine · 2025Article
- Machine Learning for Predicting Critical Events Among Hospitalized Children.JAMA network open · 2025Article
- Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI.NPJ digital medicine · 2025Article
- Assessing the Predictive Capabilities of Autoregressive Integrated Moving Average and Linear Regression Models for Acute Changes in Clinical and Selected Laboratory Parameters in Children After Cardiac Surgery in the ICU.Children (Basel, Switzerland) · 2024Article
- Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives.Biomedicines · 2024Review
- Development of a deep learning model for predicting critical events in a pediatric intensive care unit.Acute and critical care · 2024Article
- Artificial intelligence-based clinical decision support in pediatrics.Pediatric research · 2023Review
- Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care.Frontiers in pediatrics · 2022Article
- An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study.Frontiers in pediatrics · 2022Article
- Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery.Frontiers in pediatrics · 2022Article
- Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912-0.972) in the derivation cohort and 0.906 (95% CI = 0.900-0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878-0.906) in the derivation cohort and 0.845 (95% CI = 0.817-0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.
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