Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation.

ABSTRACT:

Background

Traumatic brain injury (TBI) is a critically ill disease with a high mortality rate, and clinical treatment is committed to continuously optimizing treatment strategies to improve survival rates.

Objective

This study aims to establish a reinforcement learning algorithm (RL) to optimize the survival prognosis decision-making scheme for patients with TBI in the intensive care unit.

Methods

We included a total of 2745 patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database and randomly divided them into a training set and an internal validation set at 8:2. We extracted 34 features for analysis and modeling using a 2-hour time compensation, 2 action features (mean arterial pressure and temperature), and 1 outcome feature (survival stat

SUBMITTER: Zhang H 

PROVIDER: S-EPMC12244269 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature