ArticleNPJ digital medicine2025
Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- Real-time prediction of cardiorespiratory deterioration during paediatric critical care transport using interpretable machine learning.PLOS digital health · 2026Article
- Machine Learning-Driven Prediction of Intensive Care Units Mortality and Length of Stay: A 11-Year Retrospective Study in Hong Kong Public Hospitals.Journal of medical systems · 2026Article
- Identification of Comorbidities in Obstructive Sleep Apnea Using Diverse Data and a One-Dimensional Convolutional Neural Network.Sensors (Basel, Switzerland) · 2026Observational
- Data subdivision approach enhances machine learning-based mortality prediction in pediatric ICU patients.PloS one · 2026Article
- Developing sensitive quality indicators for ground inter-hospital transport of adult critically ill patients using Donabedian model.Frontiers in public health · 2026Article
- Machine Learning for Early Detection and Prevention of Disease Using Electronic Health Records.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Unlocking the potential of real-time ICU mortality prediction: redefining risk assessment with continuous data recovery.NPJ digital medicine · 2025Article
- Identification of physiological adverse events using continuous vital signs monitoring during paediatric critical care transport: A novel data-driven approach.PLOS digital health · 2025Article
- Forecasting the Global Burden of Peripheral Artery Disease from 2021 to 2050: A Population-Based Study.Research (Washington, D.C.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Critically ill children who require inter-hospital transfers to paediatric intensive care units are sicker than other admissions and have higher mortality rates. Current transport practice primarily relies on early clinical assessments within the initial hours of transport. Real-time mortality risk during transport is lacking due to the absence of data-driven assessment tools. Addressing this gap, our research introduces the PROMPT (Patient-centred Real-time Outcome monitoring and Mortality PredicTion), an explainable end-to-end machine learning pipeline to forecast 30-day mortality risks. The PROMPT integrates continuous time-series vital signs and medical records with episode-specific transport data to provide real-time mortality prediction. The results demonstrated that with PROMPT, both the random forest and logistic regression models achieved the best performance with AUROC 0.83 (95% CI: 0.79-0.86) and 0.81 (95% CI: 0.76-0.85), respectively. The proposed model has demonstrated proof-of-principle in predicting mortality risk in transported children and providing individual-level model interpretability during inter-hospital transports.
Identifiers
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Registered trials
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.