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

Search research articles

Related Experiment Video

Updated: Dec 1, 2025

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

Predicting progression to septic shock in the emergency department using an externally generalizable machine learning

Gabriel Wardi, Morgan Carlile, Andre Holder

Medrxiv : the Preprint Server for Health Sciences

|November 11, 2020

Summary

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

Deep learning for time-series segmentation of mechanical ventilator waveforms.

Scientific reports·2026

Same author

Medical Record Abstraction for Quality Improvement in Sepsis Care Using Artificial Intelligence: A Cluster Randomized Trial.

JAMA network open·2026

Same author

Temporal Recurrent Neural Networks for Predicting Acute Kidney Injury Recovery by Time of Discharge.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same author

Development and validation of a digital biomarker for peripheral artery disease.

NPJ digital medicine·2026

Same author

A wearable electrical hemodynamic imaging ring.

ArXiv·2026

Same author

Integrating Precision Sepsis Risk Stratification Into Systems-Based Antimicrobial Stewardship.

Open forum infectious diseases·2026

Machine learning accurately predicts sepsis development in emergency departments. Transfer learning enhances algorithm generalizability, improving external validity for predicting delayed septic shock.

Area of Science:

Artificial Intelligence in Medicine
Clinical Informatics
Machine Learning for Healthcare

Background:

Machine learning (ML) algorithms enhance sepsis prediction using electronic medical records.
Transfer learning, an ML subfield, improves algorithm generalizability across clinical sites.
Validating the Artificial Intelligence Sepsis Expert (AISE) for predicting delayed septic shock is crucial.

Approach:

An observational cohort study analyzed over 180,000 patients from two academic medical centers (2014-2019).
The AISE algorithm was trained on 40 input variables to predict delayed septic shock (onset >4 hours post-ED triage).
Transfer learning was employed to validate the AISE algorithm's generalizability at a second site.

Key Points:

The AISE algorithm demonstrated high accuracy (AUC >0.8) at 8 and 12 hours for predicting delayed septic shock.

More Related Videos

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

Related Experiment Videos

Last Updated: Dec 1, 2025

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 Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

Out of 9354 severe sepsis patients, 723 developed septic shock.

Transfer learning significantly improved the AISE algorithm's external validity and performance at the validation site.

Conclusions:

The AISE algorithm accurately predicts delayed septic shock development.
Transfer learning enhances the external validity and generalizability of ML models for sepsis prediction.
Further prospective studies are recommended to assess the clinical utility of the AISE model.