Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Early Nephrology Consultation and Acute Kidney Injury in Hospitalized Patients: A Randomized Clinical Trial.

JAMA network open·2026
Same author

Care Partner's Experience with Care Received in the Emergency Department.

Convergence : breaking down barriers between disciplines : proceedings of the International Conference on Healthcare Systems Ergonomics and Patient Safety, HEPS2022. HEPS (Conference) (7th : 2022 : Delft, Netherlands)·2026
Same author

Predicting Intensive Care Readmission Among Hospitalized Children.

medRxiv : the preprint server for health sciences·2026
Same author

Falls and Fall-Related Injuries and Hospitalizations in Autistic Older Adults: A Medicare Data Study.

Autism : the international journal of research and practice·2026
Same author

Balancing Model Performance With Operational Realities in Early Warning Systems-Complexity Where It Matters.

JAMA network open·2026
Same author

Evaluation of Equity in Hospice Care Utilization Among Medicare-Enrolled Autistic Older Adults.

Autism in adulthood·2026

Related Experiment Video

Updated: May 22, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K

Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning

Matthew M Churpek1,2, Kyle A Carey3, Ashley Snyder4

  • 1Department of Medicine, University of Wisconsin-Madison, Madison, WI.

Critical Care Explorations
|March 26, 2025
PubMed
Summary

A new machine learning model, eCARTv5, significantly improves early detection of clinical deterioration in hospitalized patients compared to existing scores. This advanced early warning system shows promise for better patient outcomes and has received FDA clearance.

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Related Experiment Videos

Last Updated: May 22, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Area of Science:

  • Medical Informatics
  • Clinical Decision Support Systems
  • Machine Learning in Healthcare

Background:

  • Early detection of clinical deterioration is crucial for improving patient outcomes.
  • Existing machine learning early warning scores often lack rigorous validation and subgroup analysis.
  • Traditional logistic regression models have limitations in capturing complex patient data.

Purpose of the Study:

  • To develop and prospectively validate a gradient-boosted machine model, eCARTv5, for identifying clinical deterioration in hospitalized patients.
  • To compare the performance of eCARTv5 against established early warning scores like MEWS and NEWS.
  • To ensure the model's robustness across diverse patient populations and clinical scenarios.

Main Methods:

  • Utilized a gradient-boosted trees algorithm with predictor variables including demographics, vital signs, documentation, and laboratory values.
  • Developed the model (eCARTv5) using a large dataset from adult patients in inpatient medical-surgical wards (2006-2022).
  • Externally validated the model retrospectively (2009-2023) and prospectively (2023-2024) across multiple health systems.

Main Results:

  • eCARTv5 demonstrated superior performance with the highest Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.834 in retrospective validation.
  • Outperformed eCARTv2 (0.775), NEWS (0.766), and MEWS (0.704) in retrospective validation.
  • Maintained high performance (AUROC ≥0.80) across various patient demographics, clinical conditions, and in prospective validation.

Conclusions:

  • The developed eCARTv5 model offers improved accuracy in identifying clinical deterioration compared to existing scores.
  • eCARTv5's validated performance across diverse subgroups and prospective testing supports its clinical utility.
  • The study's findings provided the basis for FDA clearance, enabling eCARTv5's use in clinical settings for hospitalized ward patients.