Journal of medical Internet research · 2025
Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation.
СтатьяВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Zhang Hongwei, Diao Mengyuan, Zhang Sheng, Ni Peifeng, Zhang Weidong, Wu Chenxi, Zhu Ying, Hu Wei
Аннотация
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 status at 28 d). We used an RL algorithm called weighted dueling double deep Q-network with embedded human expertise to maximize cumulative returns and evaluated the model using a doubly robust off-policy evaluation method. Finally, we collected 2463 patients with TBI from MIMIC III as an external validation set to test the model.
RESULTS: The action features are divided into 6 intervals, and the expected benefits are estimated using a doubly robust off-policy evaluation method. The results indicate that the survival rate of artificial intelligence (AI) strategies is higher than that of clinical doctors (88.016%, 95% CI 85.191%-90.840% vs 81.094%, 95% CI 80.422%-81.765%), with an expected return of 28.978 (95% CI 28.797-29.160) versus 27.092 (95% CI 24.584-29.600). Compared with clinical doctors, AI algorithms select normal temperatures more frequently (36.56 °C to 36.83 ℃) and recommend mean arterial pressure levels of 87.5-95.0 mm Hg. In external validation, the AI strategy still has a high survival rate of 87.565%, with an expected return of 27.517.
CONCLUSIONS: This RL algorithm for patients with TBI indicates that a more personalized and targeted optimization of the vital signs is possible. This algorithm will assist clinicians in making decisions on an individualized patient-by-patient basis.
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 status at 28 d). We used an RL algorithm called weighted dueling double deep Q-network with embedded human expertise to maximize cumulative returns and evaluated the model using a doubly robust off-policy evaluation method. Finally, we collected 2463 patients with TBI from MIMIC III as an external validation set to test the model.
RESULTS: The action features are divided into 6 intervals, and the expected benefits are estimated using a doubly robust off-policy evaluation method. The results indicate that the survival rate of artificial intelligence (AI) strategies is higher than that of clinical doctors (88.016%, 95% CI 85.191%-90.840% vs 81.094%, 95% CI 80.422%-81.765%), with an expected return of 28.978 (95% CI 28.797-29.160) versus 27.092 (95% CI 24.584-29.600). Compared with clinical doctors, AI algorithms select normal temperatures more frequently (36.56 °C to 36.83 ℃) and recommend mean arterial pressure levels of 87.5-95.0 mm Hg. In external validation, the AI strategy still has a high survival rate of 87.565%, with an expected return of 27.517.
CONCLUSIONS: This RL algorithm for patients with TBI indicates that a more personalized and targeted optimization of the vital signs is possible. This algorithm will assist clinicians in making decisions on an individualized patient-by-patient basis.