ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
Prediction of Resuscitation for Pediatric Sepsis from Data Available at Triage.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis.Pediatric research · 2026Article
- Predicting sepsis treatment decisions in the paediatric emergency department using machine learning: the AiSEPTRON study.BMJ paediatrics open · 2025Observational
- Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives.Biomedicines · 2024Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pediatric sepsis imposes a significant burden of morbidity and mortality among children. While the speedy application of existing supportive care measures can substantially improve outcomes, further improvements in delivering that care require tools that go beyond recognizing sepsis and towards predicting its development. Machine learning techniques have great potential as predictive tools, but their application to pediatric sepsis has been stymied by several factors, particularly the relative rarity of its occurrence. We propose an alternate approach which focuses on predicting the provision of resuscitative care, rather than sepsis diagnoses or criteria themselves. Using three years of Emergency Department data from a large academic medical center, we developed a boosted tree model that predicts resuscitation within 6 hours of triage, and significantly outperforms existing rule-based sepsis alerts.
Indexed as
Identifiers
35308977PMC8861694What OpenQuestion holds
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.