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Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and
Hongwei Zhang1, Mengyuan Diao1, Sheng Zhang2
1Department of Critical Care Medicine, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, 261 Huansha Road, Hangzhou, 310006, China, 86 13634164536.
Journal of Medical Internet Research
|July 3, 2025
Summary
This study developed an AI algorithm using reinforcement learning (RL) to improve survival rates for traumatic brain injury (TBI) patients. The AI strategy demonstrated a higher survival rate compared to clinical doctors, offering personalized treatment recommendations.
Area of Science:
- Intensive Care Medicine
- Artificial Intelligence
- Clinical Decision Support
Background:
- Traumatic brain injury (TBI) presents a high mortality rate, necessitating continuous optimization of clinical treatment strategies.
- Improving survival rates for critically ill TBI patients remains a significant challenge in intensive care units.
Purpose of the Study:
- To develop a reinforcement learning (RL) algorithm for optimizing survival prognosis decision-making in intensive care unit (ICU) patients with TBI.
- To enhance patient outcomes by creating a data-driven approach to TBI management.
Main Methods:
- Utilized the Medical Information Mart for Intensive Care (MIMIC)-IV database, including 2745 TBI patients, with a training/internal validation split (8:2).
- Extracted 34 features, including mean arterial pressure and temperature, with survival status at 28 days as the outcome.
- Employed a weighted dueling double deep Q-network RL algorithm, incorporating human expertise and evaluated using doubly robust off-policy evaluation. External validation performed on MIMIC III data.
Main Results:
- The AI strategy achieved a higher survival rate (88.016%) compared to clinical doctors (81.094%) in internal validation.
- AI recommended optimal temperature ranges (36.56°C–36.83°C) and mean arterial pressure (87.5–95.0 mm Hg).
- External validation confirmed the AI strategy's effectiveness with a survival rate of 87.565%.
Conclusions:
- The developed RL algorithm enables personalized and targeted optimization of vital signs for TBI patients.
- This AI-driven approach can serve as a valuable tool to assist clinicians in making individualized patient care decisions.
