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Related Experiment Video

Updated: Sep 10, 2025

Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care
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Predicting Episodes of Hypovigilance in Intensive Care Units Using Routine Physiological Parameters and Artificial

Raphaëlle Giguère1,2, Victor Niaussat3,4, Monia Noël-Hunter2

  • 1Department of Computer Sciences, Faculty of Sciences and Engineering, Université Laval, Québec, QC, Canada.

JMIR AI
|August 27, 2025
PubMed
Summary

Machine learning models can now detect hypovigilance, a subtle sign of hypoactive delirium in intensive care units (ICUs). This AI approach aids in early detection, improving patient outcomes by identifying subtle clinical signs.

Keywords:
prediction modelICUartificial intelligenceautomated monitoringdeliriumdetectiondetection modelhyperactivehypoactive deliriumhypovigilanceintensive care unitmachine learningmonitoringphysiological parametersvigilance

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Area of Science:

  • Artificial Intelligence
  • Intensive Care Medicine
  • Machine Learning

Background:

  • Hypoactive delirium is common and difficult to detect in ICUs.
  • Subtle signs like hypovigilance pose diagnostic challenges.
  • Existing tools lack reliability for timely hypoactive delirium identification.

Purpose of the Study:

  • To develop an AI prediction model for detecting hypovigilance events.
  • Utilize routinely collected physiological data in the ICU for prediction.
  • Support early identification of hypoactive delirium.

Main Methods:

  • Prospective observational cohort study in an ICU.
  • Collected physiological data (vitals, etc.) and clinical variables.
  • Trained Random Forest, XGBoost, and Light Gradient Boosting Machine models on time series data.
  • Used SHAP analysis for model interpretation.

Main Results:

  • Light Gradient Boosting Machine showed best performance (68% accuracy).
  • Key predictors included intubation status, respiratory rate, and blood pressure.
  • Models achieved 76% precision and 74% recall for hypovigilance detection.

Conclusions:

  • Machine learning models show potential for detecting hypovigilance.
  • These algorithms can aid in the early detection of hypoactive delirium.
  • Further development is warranted based on classifier performance.