Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Barriers and opportunities to improve fluid balance recognition and reduction: a qualitative study.

Frontiers in pediatrics·2026

Same author

Divergence Between Net Fluid and Weight-Based Evaluation in Calculating Cumulative Fluid Balance.

Critical care explorations·2026

Same author

Aberrant STAT signaling and T cell dysregulation define a targetable pediatric sepsis endotype.

The Journal of clinical investigation·2026

Same author

Generative intelligence explores the chemical space of ten million catalysts.

Chemical science·2026

Same author

PPARγ Variant rs10865710 and Mortality in Pediatric Septic Shock Stratified by Corticosteroid Exposure.

Critical care explorations·2026

Same author

External Validation, Molecular Signatures, and Therapeutic Relevance of Pediatric Sepsis-Associated Acute Kidney Injury Subphenotypes.

Critical care medicine·2026

Same journal

Establishment of a CT-based prediction model for endotracheal tube size in infants aged <1 year.

Frontiers in pediatrics·2026

Same journal

Case Report: Subcutaneous sensor-augmented insulin pump therapy in a 510-g preterm infant with hyperglycemia.

Frontiers in pediatrics·2026

Same journal

Association between glucolipid metabolic indicators and the risk of suspected precocious puberty in children living with obesity: a retrospective cohort study.

Frontiers in pediatrics·2026

Same journal

Clinical and imaging features of tufted angioma in children.

Frontiers in pediatrics·2026

Same journal

Transcriptome-Inferred metabolic subtypes define prognostic and immune ecosystems in osteosarcoma at single-cell resolution.

Frontiers in pediatrics·2026

Same journal

Efficacy and anthropometric predictors of negative-pressure therapy for recurrent concealed penis in pediatric patients.

Frontiers in pediatrics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 4, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Pediatric Severe Sepsis Prediction Using Machine Learning.

Sidney Le¹, Jana Hoffman¹, Christopher Barton^1,2

¹Dascena Inc., Oakland, CA, United States.

Frontiers in Pediatrics

|November 5, 2019

Summary

This summary is machine-generated.

A machine learning algorithm can predict severe sepsis in children using electronic health records (EHR). This AI tool shows promise for earlier detection and treatment of pediatric severe sepsis.

Keywords:

early detection electronic health records machine learning pediatric severe sepsis prediction

More Related Videos

A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis

A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis

Published on: August 12, 2020

A Controlled Mouse Model for Neonatal Polymicrobial Sepsis

A Controlled Mouse Model for Neonatal Polymicrobial Sepsis

Published on: January 27, 2019

Related Experiment Videos

Last Updated: Jan 4, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis

A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis

Published on: August 12, 2020

A Controlled Mouse Model for Neonatal Polymicrobial Sepsis

A Controlled Mouse Model for Neonatal Polymicrobial Sepsis

Published on: January 27, 2019

Area of Science:

Pediatric critical care medicine
Health informatics
Machine learning in healthcare

Background:

Early detection of pediatric severe sepsis is crucial for effective treatment.
Novel methods are needed to improve the early identification of severe sepsis in children.
Machine learning offers a potential avenue for enhancing sepsis detection.

Purpose of the Study:

To evaluate a machine learning algorithm's ability to predict severe sepsis onset in pediatric patients.
To assess the algorithm's performance using electronic healthcare record (EHR) data.
To compare the algorithm's predictive power against existing clinical scores.

Main Methods:

Retrospective analysis of de-identified EHR data from pediatric inpatient and emergency encounters (ages 2-17).
Data collected from UCSF Medical Center between June 2011 and March 2016.
A machine learning algorithm was developed and evaluated using 4-fold cross-validation.

Main Results:

The study identified 101 cases of severe sepsis among 9,486 pediatric patients.
The machine learning algorithm achieved an AUROC of 0.916 at sepsis onset and 0.718 at 4 hours prior.
The algorithm significantly outperformed PELOD-2 and SIRS scores in predicting severe sepsis 4 hours before onset.

Conclusions:

A machine learning algorithm utilizing EHR data can effectively detect and predict pediatric severe sepsis.
Automated monitoring of EHR data holds potential for earlier sepsis recognition and treatment initiation in pediatric inpatients.
This approach may significantly improve outcomes for children with severe sepsis.