Pediatric Severe Sepsis Prediction Using Machine Learning

Sidney Le, Jana L. Hoffman, Christopher Barton, Julie C. Fitzgerald, Angier O. Allen, Emily Pellegrini, Jacob S. Calvert, Ritankar Das

Frontiers in Pediatrics 2019 article cited by 110

Source Open-access PDF More in Medicine & Health

importance 7/10 ai assisted Sepsis Diagnosis and Treatment pediatric critical care stage: research

Problem

Severe sepsis in hospitalized children is hard to detect early enough to optimize effective treatment.

Why it matters

Delayed sepsis detection in children can be fatal; earlier alerts enable timely intervention.

What the AI did

A machine learning algorithm used retrospective EHR data from pediatric inpatient and emergency encounters to predict severe sepsis onset.

Results

AUROC 0.916 in four-fold cross-validation (n=9,486 patients; 101 severe sepsis cases)

Impact

Retrospective validation shows strong discrimination; no prospective deployment stated in source.

Record

Venue	Frontiers in Pediatrics
Published	2019
Work type	article
Authors	Sidney Le, Jana L. Hoffman, Christopher Barton, Julie C. Fitzgerald, Angier O. Allen, Emily Pellegrini, Jacob S. Calvert, Ritankar Das
Citations	110 (OpenAlex, at import time)
References	50
Model or method	not stated in source
Organization	University of California San Francisco (UCSF) Medical Center (data source)
Primary source	https://doi.org/10.3389/fped.2019.00413
Open access	gold
Keywords	sepsis, machine learning, EHR, pediatrics, prediction

Importance rating

7 / 10

significant — thousands to millions affected

Severe sepsis in hospitalized children is fatal if detected late; abstract frames early detection as necessary for effective treatment, implying major clinical stakes, but gives no population scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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