Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.

ABSTRACT:

Background

Delirium in intensive care unit (ICU) patients poses a significant challenge, affecting patient outcomes and health care efficiency. Developing an accurate, real-time prediction model for delirium represents an advancement in critical care, addressing needs for timely intervention and resource optimization in ICUs.

Objective

We aimed to create a novel machine learning model for delirium prediction in ICU patients using only continuous physiological data.

Methods

We developed models integrating routinely available clinical data, such as age, sex, and patient monitoring device outputs, to ensure practicality and adaptability in diverse clinical settings. To confirm the reliability of delirium determination records, we prospectively collected results of Confus

SUBMITTER: Park C 

PROVIDER: S-EPMC12004028 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature