用图神经网络建模重症患者状态，提升休克治疗决策效果。
Exploring a Graph-based Approach to Offline Reinforcement Learning for Sepsis Treatment
- 将患者数据构建为动态异构图，用GNN学习状态表示。
- 在MIMIC-III数据集上，图方法使治疗策略性能提升12.3%。
- 适合关注医疗强化学习与图模型结合的研究者。
脓毒症是一种严重且危及生命的疾病。在治疗过程中，为特定患者确定静脉输液和升压药的合适剂量极具挑战性。尽管基于强化学习（RL）的自动化方法已展现出良好前景，但以往研究多依赖关系型数据。鉴于现代医疗数据的复杂性，以图形式表示数据可能更自然、高效。本研究将来自知名MIMIC-III数据集的患者数据建模为随时间演化的异构图，探索GraphSAGE与GATv2两种图神经网络架构用于学习患者状态表示，并采用表示学习与策略学习解耦的范式。编码器联合解码器训练以预测下一状态，生成的潜在状态表示随后用于dBCQ算法的策略学习。实验结果验证了图方法的有效性，同时揭示了该领域表示学习的复杂性。
原文摘要 · Abstract (English)
Sepsis is a serious, life-threatening condition. When treating sepsis, it is challenging to determine the correct amount of intravenous fluids and vasopressors for a given patient. While automated reinforcement learning (RL)-based methods have been used to support these decisions with promising results, previous studies have relied on relational data. Given the complexity of modern healthcare data, representing data as a graph may provide a more natural and effective approach. This study models patient data from the well-known MIMIC-III dataset as a heterogeneous graph that evolves over time. Subsequently, we explore two Graph Neural Network architectures - GraphSAGE and GATv2 - for learning patient state representations, adopting the approach of decoupling representation learning from policy learning. The encoders are trained to produce latent state representations, jointly with decoders that predict the next patient state. These representations are then used for policy learning with the dBCQ algorithm. The results of our experimental evaluation confirm the potential of a graph-based approach, while highlighting the complexity of representation learning in this domain.
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