AI 中文总结
该研究针对机械通气决策的RL方法缺失笔记临床信息的问题，提出冗余感知多模态框架，通过两种策略去除笔记冗余，在ICU数据上显著提升了RL临床决策性能。
AI 中文摘要
机械通气是一种关键的生命支持干预措施，需随患者病情变化动态调整呼吸机设置。尽管强化学习（RL）为优化这些序贯决策提供了极具前景的框架，但标准方法主要依赖结构化电子健康记录（EHR）数据，遗漏了自由文本笔记中记录的关键临床背景。将纵向临床笔记整合到RL状态空间颇具挑战，因为笔记充斥着大量时间性冗余，如复制转发文本、模板化内容及重复记录，这会削弱时间局部更新效果并降低状态表示质量。为解决该问题，我们提出一种感知冗余的多模态状态表示框架，在策略学习前明确去除随时间重复的笔记文本。我们评估了两种计算高效的时间分解策略以去除重复笔记文本：（1）在局部历史子空间上使用奇异值分解的嵌入空间分解；（2）可解释的句子级差分操作，在文本编码前过滤掉先前记录的句子。利用真实世界ICU数据，我们证明，通过剥离时间性笔记冗余构建的状态表示，在多种离策略评估方法（基于模型的滚动、拟合Q评估、加权重要性采样及加权双重鲁棒评估）中，显著优于仅结构化数据和原始笔记的基线。我们的发现表明，从重复笔记文本中明确分离新临床信息，可产生更高质量的状态表示，并直接提升临床决策支持的RL性能。
英文摘要
Mechanical ventilation is a critical life-support intervention, requiring dynamic adjustments to ventilator settings as a patient's condition evolves. While reinforcement learning (RL) offers a promising framework for optimizing these sequential decisions, standard approaches rely primarily on structured electronic health record (EHR) data, missing crucial clinical context recorded in free-text notes. Integrating longitudinal clinical notes into RL state spaces is challenging because notes are heavily inflated by temporal redundancy, such as copy-forward text, templating, and repetitive documentation, which dilutes time-local updates and degrades state representation quality. To address this, we propose a redundancy-aware multimodal state representation framework that explicitly removes duplicated note text over time before policy learning. We evaluate two computationally efficient temporal decomposition strategies for removing duplicated note text: (1) an embedding-space decomposition using singular value decomposition on local history subspaces, and (2) an interpretable sentence-level diff operation that filters out previously documented sentences before text encoding. Using real-world ICU data, we demonstrate that state representations constructed by stripping temporal note redundancy significantly outperform both structured-only and raw-note baselines across multiple off-policy evaluation methods (Model-Based Rollouts, Fitted Q-Evaluation, Weighted Importance Sampling, and Weighted Doubly Robust Evaluation). Our findings show that explicitly isolating new clinical information from repeated note text yields higher-quality state representations and directly improves RL performance for clinical decision support.
