# Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model

## TL;DR

This study develops a machine learning model to automatically assess agitation and sedation levels in ICU patients, improving efficiency and safety.

## Contribution

The study introduces an ensemble learning model that enhances agitation sensitivity while maintaining high accuracy in ICU assessments.

## Key findings

- The random forest model achieved high AUC values for sedation (0.97) and agitation (0.88) classification.
- Ensemble learning improved agitation sensitivity to 0.82 while maintaining AUC values above 0.82 across all categories.
- Model explanations using SHAP aligned with clinical experience, supporting model trustworthiness.

## Abstract

Agitation and sedation management is critical in intensive care as it affects patient safety. Traditional nursing assessments suffer from low frequency and subjectivity. Automating these assessments can boost intensive care unit (ICU) efficiency, treatment capacity, and patient safety.

The aim of this study was to develop a machine-learning based assessment of agitation and sedation.

Using data from the Taichung Veterans General Hospital ICU database (2020), an ensemble learning model was developed for classifying the levels of agitation and sedation. Different ensemble learning model sequences were compared. In addition, an interpretable artificial intelligence approach, SHAP (Shapley additive explanations), was employed for explanatory analysis.

With 20 features and 121,303 data points, the random forest model achieved high area under the curve values across all models (sedation classification: 0.97; agitation classification: 0.88). The ensemble learning model enhanced agitation sensitivity (0.82) while maintaining high AUC values across all categories (all >0.82). The model explanations aligned with clinical experience.

This study proposes an ICU agitation-sedation assessment automation using machine learning, enhancing efficiency and safety. Ensemble learning improves agitation sensitivity while maintaining accuracy. Real-time monitoring and future digital integration have the potential for advancements in intensive care.