Related Experiment Video

Updated: Jun 13, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
19:15

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

86.1K

Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based

Patricia Gilholm1, Kristen Gibbons1, Sarah Brüningk2,3

  • 1Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia.

Intensive Care Medicine
|June 24, 2023
PubMed

Insights

A new machine learning model predicts poor school outcomes for children after intensive care unit (ICU) stays. This tool helps identify children needing extra support for better long-term educational success.

Area of Science:

  • Pediatric critical care medicine
  • Machine learning in healthcare
  • Educational outcome prediction

Background:

  • Improved survival rates in pediatric intensive care units (ICUs) highlight the need for tools to predict long-term outcomes.
  • Current methods lack the ability to forecast educational challenges for childhood ICU survivors.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting poor school outcomes in children following ICU admission.
  • To identify key factors contributing to educational difficulties in pediatric ICU survivors.

Main Methods:

  • A population-based study analyzed data from children under 16 admitted to ICUs in Queensland, Australia (1997-2019).
  • The primary outcome was failure to meet the National Minimum Standard (NMS) in the National Assessment Program-Literacy and Numeracy (NAPLAN) tests.
  • Machine learning classifiers were trained and validated using routine ICU data and stratified nested cross-validation.

Main Results:

  • The study included 13,957 ICU survivors with 37,200 NAPLAN tests over a median 6-year follow-up.
  • The model achieved an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.8, predicting NMS failure with 85% sensitivity and 51% specificity.
  • Socio-economic status, illness severity, and neurological, congenital, and genetic disorders were significant predictors.

Conclusions:

  • A machine learning model utilizing data available at ICU discharge can predict educational difficulties in school-aged children.
  • This predictive tool can aid in prioritizing patients for follow-up care and targeted rehabilitation interventions.
  • The model offers a promising approach to support the long-term well-being of pediatric ICU survivors.
Abstract

Keywords:
ChildIntensive careMachine learningNeurodevelopmentSchool

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma04:09

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

Published on: October 10, 2018

8.3K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack07:31

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

Published on: May 15, 2020

7.1K

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...