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REM Sleep Behavior Disorder

REM Sleep Behavior Disorder

REM Sleep Behavior Disorder (RBD) is a sleep disorder characterized by the absence of muscle paralysis that normally occurs during the REM phase of sleep. This absence allows individuals to physically act out their dreams, which are often vivid and disturbing. Common behaviors exhibited during episodes include kicking, punching, and yelling. These actions can be dangerous, potentially leading to injuries for the person with RBD or their bed partner.
RBD is significantly associated with...

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Deep Learning-Based Recurrent Delirium Prediction in Critically Ill Patients.

Filipe R Lucini^1,2, Henry T Stelfox^1,3, Joon Lee^2,3,4,5,6

¹Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

Critical Care Medicine

|February 15, 2023

Summary

This study developed a deep learning model to predict intensive care unit (ICU) delirium. The model accurately forecasts delirium episodes, enabling timely interventions and improved patient care.

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Area of Science:

Artificial Intelligence in Medicine
Critical Care Medicine
Health Informatics

Background:

Delirium is a common complication in intensive care units (ICUs), associated with adverse patient outcomes.
Early prediction of delirium is crucial for timely intervention and resource management.

Purpose of the Study:

To develop and validate a recurrent deep learning model for predicting impending delirium in ICU patients.
To leverage a large-scale dataset and temporal features for enhanced prediction accuracy.

Main Methods:

A retrospective cohort study utilizing ICU and administrative health data from 43,510 ICU admissions.
Development of a deep learning architecture with feature embedding, recurrent, and prediction modules.
Training models to predict delirium episodes in 0-12 and 12-24 hour horizons using 3,643 temporal features.

Main Results:

The best gated recurrent unit model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.909 for 0-12 hour predictions and 0.895 for 12-24 hour predictions.
The model demonstrated high sensitivity (0.810) and specificity (0.848) for the 0-12 hour prediction horizon.
Precision values were 0.704 (0-12 hr) and 0.637 (12-24 hr), indicating reliable positive predictions.

Conclusions:

Deep learning applied to a large, temporally rich dataset enables accurate prediction of ICU delirium.
The developed model offers potential for early intervention, optimized resource allocation, and improved patient outcomes in ICUs.
This study represents a significant advancement over previous delirium prediction research due to dataset size and feature complexity.