项目概要
PICU患儿呼吸衰竭是一种常见的情况，其发病原因包括气道阻塞、肺组织疾病、心脏疾病等。随着疾病的发展会造成躁动不安、神志不清以及昏迷，若不及时处理会导致病情更加严重，甚至会危及生命。呼吸衰竭需要根据医生经验及时上呼吸机对患儿进行机械通气、开展药物治疗并进行生命体征监护，当前诊疗手段面临着测评不便、诊治失据、医资短缺等难题。为此，本项目拟开展基于多模态的PICU呼吸衰竭患儿机械通气辅助诊疗关键技术研究与应用，突破多模态诊疗技术在医学应用中的壁垒，实现对呼吸衰竭患儿诊疗的技术创新和模式创新，促进医工交叉及临床服务水平提升，为健康中国建设提供坚实的科技支撑。
主要研究内容
- 基于强化学习的呼吸机参数精准控制算法研发
- 针对医疗场景治疗策略的决策安全性需求，对危险动作进行安全约束
- 基于连续状态的强化学习辅助决策算法研究
- 针对离线策略学习无法直接迁移到实际环境的问题，研究降低策略分布偏移影响的方法
- 针对离线策略评估问题，研究更合理的策略评估方法
相关阅读
研究成果
Journal Articles
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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
Health Inf Sci Syst
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. This algorithm integrates clinical expertise in ventilator tuning via Arterial Blood Gas (ABG) analysis with data-driven methods. We perform K-means clustering algorithm on patient dataset based on ABG values for the first time, and the classified data was used to train a deep offline reinforcement learning model based on conservative Q-learning (CQL), therefore we named it the K-CQL algorithm. The introduction of human expert knowledge improves the effectiveness of the entire model. 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
项目人员
赵云波 席嘉滢 徐晨伟