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 Experiment Videos

Predicting Pediatric Mortality Across Five Intensive Care Units: Toward an Early Warning Using Machine Learning.

Kseniia Sholokhova1, Yu-Chuan Li1, Chih-Wei Huang1

  • 1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Steps in Outbreak Investigation01:18

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

An evidence gap map of digital health interventions for enhancing patient engagement in healthcare.

NPJ digital medicine·2026
Same author

A Benzonitrile-Based Sodium Coordination Polymer with UV-Absorbing Properties: Synthesis, Characterization, Crystallography, and Thin-Film Preparations.

Inorganic chemistry·2026
Same author

Interpretable Machine Learning Framework for Predicting Major Adverse Cardiovascular Events in Rheumatoid Arthritis Using Electronic Health Records: Multicenter Cohort Study.

JMIR formative research·2026
Same author

AI-Integrated EEG Decision Support for Neurocritical Care: A Conceptual and Feasibility Framework.

Studies in health technology and informatics·2026
Same author

Disease associated missense substitutions disrupt structural stability and catalytic function of 4-hydroxyphenylpyruvate dioxygenase.

The Journal of biological chemistry·2026
Same author

An exploratory study of multi-channel CNN for early detection of lung cancer from longitudinal healthcare records.

Scientific reports·2026
Same journal

The Essential Components and Critical Conditions for Success in a Learning Health System in Oncology.

Studies in health technology and informatics·2026
Same journal

Use of Artificial Intelligence in Screening for Adolescent Idiopathic Scoliosis: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Movement Related Biomechanics in Adolescent Idiopathic Scoliosis: A Review of Reviews.

Studies in health technology and informatics·2026
Same journal

The Impact of Surgical Correction of Adolescent Idiopathic Scoliosis Using Posterior Spinal Fusion on Selected Radiological Parameters and Respiratory Function.

Studies in health technology and informatics·2026
Same journal

Acute Effect of Physio-logic® Exercises on Muscle Tone and Stiffness in Adolescent Idiopathic Scoliosis Patients: A Preliminary Study.

Studies in health technology and informatics·2026
Same journal

Effects of Integrated Music and Occupational Therapy on Motor and Autonomic Function in Children with Neurogenic Scoliosis.

Studies in health technology and informatics·2026
See all related articles

Developing unit-specific machine learning models aids early identification of high-risk children in pediatric intensive care units (ICUs). These data-driven systems show strong performance in predicting mortality risk.

Area of Science:

  • Pediatric critical care medicine
  • Machine learning applications in healthcare
  • Biomedical data science

Background:

  • Early identification of high-mortality risk in pediatric intensive care units (ICUs) remains a challenge.
  • Heterogeneous ICU settings complicate risk stratification efforts.
  • Existing methods may not fully leverage complex patient data for timely intervention.

Purpose of the Study:

  • To develop and evaluate unit-specific machine learning (ML) models for early mortality risk prediction in pediatric ICUs.
  • To assess the feasibility of data-driven early-warning systems tailored to different ICU environments.
  • To identify key clinical and laboratory predictors of mortality in pediatric critical care.

Main Methods:

  • Trained Random Forest (RF) classifiers separately for surgical (SICU), cardiac (CICU), general, neonatal (NICU), and pediatric (PICU) wards.
Keywords:
Pediatric intensive carechildhood mortalitymachine learning

Related Experiment Videos

  • Utilized admission data including demographics, diagnoses, medications, and laboratory features.
  • Evaluated model performance using Area Under the Curve (AUC) metrics.

    • Main Results:

    • Achieved strong discriminative performance with AUC values ranging from 0.86 to 0.97 across different units.
    • Identified key predictors of mortality, including Lactate (LAC), red-cell distribution width (RDW), platelets (PLT), hemoglobin (Hb), and creatinine (Cr).
    • Demonstrated the effectiveness of unit-specific ML models in diverse pediatric ICU settings.

    Conclusions:

    • Unit-specific machine learning models are feasible and effective for early mortality risk prediction in pediatric ICUs.
    • Data-driven early-warning systems can enhance clinical decision-making and patient management.
    • Key laboratory markers play a significant role in identifying high-risk pediatric patients.