Observational studyCritical care (London, England)2019
A deep learning model for real-time mortality prediction in critically ill children.
Observational study in Critical care (London, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review.Journal of medical Internet research · 2025Pooled it
- AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Artificial Intelligence in Predicting Mortality Risk for Critically Ill Children in Pediatric Intensive Care Units: A Systematic Review.Health science reports · 2026Review
- Machine Learning-Based Prediction of Critical Deterioration in the PICU.Critical care explorations · 2026Article
- Data subdivision approach enhances machine learning-based mortality prediction in pediatric ICU patients.PloS one · 2026Article
- Development and Validation of a Nomogram Model to Predict the Risk of Severe Pneumonia in Children with Pneumococcal Infection.Infection and drug resistance · 2026Article
- Unlocking the potential of real-time ICU mortality prediction: redefining risk assessment with continuous data recovery.NPJ digital medicine · 2025Article
- Weight variability at pediatric intensive care unit admission and adverse outcomes in critically ill children.Acute and critical care · 2025Article
- Vital Signs-Only Machine Learning Model for Acute Inpatient Deterioration: A Retrospective Multicenter Study.Mayo Clinic proceedings. Innovations, quality & outcomes · 2025Article
- Article
- Prediction cardiovascular deterioration in a paediatric intensive care unit (PicEWS): a machine learning modelling study of routinely collected health-care data.EClinicalMedicine · 2025Article
- Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis.BMC public health · 2025Article
- Machine Learning for Predicting Critical Events Among Hospitalized Children.JAMA network open · 2025Article
- Review
- Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI.NPJ digital medicine · 2025Article
- Machine learning-based time-to-event survival analysis in pediatric patients with severe sepsis.Frontiers in pediatrics · 2025Article
- Role of artificial intelligence in pediatric intensive care: a survey of healthcare staff perspectives in Saudi Arabia.Frontiers in pediatrics · 2025Article
- A Novel Situational Awareness Scoring System in Pediatric Cardiac Intensive Care Unit Patients.Journal of pediatric intensive care · 2024Article
- Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data.BMJ paediatrics open · 2024Article
- Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundThe rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units.
methodsUtilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center.
resultsUsing seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89-0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts.
conclusionsPROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients.
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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.