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K-CQL: An Arterial Blood Gas Analysis-Based Deep Offline Reinforcement Learning Algorithm for Mechanical Ventilation Treatment Jiaying Xi, Shaojie Dong, Haoquan Zhou, and Yunbo Zhao 2025 [Abs] [doi] [pdf]
Mechanical ventilation is employed as a supportive therapy for patients with respiratory failure, but the optimal ventilator settings for patient are often unknown and rely on manual adjustment by physicians. Improper parameter settings may lead to severe complications such as lung injury. To personalize mechanical ventilation and predict the optimal ventilator parameters for patients, we propose a ventilator parameter tuning algorithm called K-CQL. This algorithm integrates clinical expertise in ventilator tuning via Arterial Blood Gas (ABG) analysis with data-driven methods. We classified patient dataset based on ABG values for the first time, and the classified data was used to train a deep reinforcement learning model based on conservative Q-learning (CQL). Our evaluation based on Fitted Q Evaluation (FQE) on the MIMIC-III dataset shows that the expected return of the output strategy of K-CQL is 1.76 times that of the physicians, and more importantly, the introduction of intermediate rewards related to ABG analysis further improves it. We also demonstrated that the algorithm is capable of recommending mechanical ventilation parameters within a safe range according to clinical nursing standards.