A deep learning model for estimating sedation levels using heart rate variability and vital signs: a retrospective cross-sectional study at a center in South Korea.

ABSTRACT:

Background

Optimal sedation assessment in critically ill children remains challenging due to the subjective nature of behavioral scales and intermittent evaluation schedules. This study aimed to develop a deep learning model based on heart rate variability (HRV) parameters and vital signs to predict effective and safe sedation levels in pediatric patients.

Methods

This retrospective cross-sectional study was conducted in a pediatric intensive care unit at a tertiary children's hospital. We developed deep learning models incorporating HRV parameters extracted from electrocardiogram waveforms and vital signs to predict Richmond Agitation-Sedation Scale (RASS) scores. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and ar

SUBMITTER: Kim YS 

PROVIDER: S-EPMC11617840 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature