发表机构
Chongqing Institute of Green and Intelligent Technology, CAS; Chongqing School, University of Chinese Academy of Sciences; Department of Anesthesiology, Southwest Hospital, Third Military Medical University (Army Medical University); College of Computer and Information Science, Southwest University; Institute of Software, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究院; 中国科学院大学重庆学院; 陆军军医大学（第三军医大学）西南医院麻醉科; 西南大学计算机与信息科学学院; 中国科学院软件研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对RRT策略优化中数据异质与缺失问题，提出面向AI的CNA健康评估，结合23维状态与矩阵分解，通过离线强化学习将死亡率降低62.24%，住院时间缩短18.93%。
AI 中文摘要
全球数百万人需要肾脏替代治疗（RRT）作为维持生命的关键治疗手段。然而，由于患者动态的异质性、数据缺失以及缺乏面向AI的健康评估标准，通过AI优化RRT策略面临挑战。我们提出了一种面向AI的健康状态综合归一化评估（CNA），并将其应用于通过离线强化学习（RL）优化RRT策略。CNA的关键思想是将生命体征分布转换为标准正态空间，从而能够基于与参考区间的偏差定义一个统一的、数据驱动的健康状态评分，该评分同时提供了评估策略质量的AI导向标准，并支持RL的终止条件。我们进一步设计了一个结构化的23维状态表示，整合了19项指标和4个RRT描述符，并采用矩阵分解来重建缺失的生命体征，提高了学习数据的完整性。这些组件被整合到多种离线RL算法中，并通过在RRT特征子集上的系统性消融研究进行了验证。与医生的观察性治疗相比，学习到的最佳策略将死亡率从13.2%降至5.0%（降低了62.24%），并将平均住院时间从308.5小时缩短至250.1小时（缩短了18.93%），展示了方法论创新以及CNA引导的RL在改善肾脏病学RRT结局方面的潜力。
英文摘要
Millions worldwide require Renal Replacement Therapy (RRT) as a treatment essential for survival. However, optimizing RRT strategies via AI is challenging due to heterogeneous patient dynamics, missing data, and the absence of an AI-oriented health assessment criterion. We propose an AI-Oriented Comprehensive Normalized Assessment (CNA) for healthy status and apply it to optimize RRT strategies by using offline reinforcement learning (RL). The key idea of CNA is transforming vital-sign distributions into a standard normal space, enabling a unified, data-driven health-status score defined by deviations from referent intervals, which also provides an AI-oriented criterion to assess strategy quality and supports RL termination. We further design a structured 23-dimensional state representation that integrates 19 indicators with 4 RRT descriptors, and employ matrix decomposition to reconstruct missing vital signs, improving data completeness for learning. These components are incorporated into multiple offline RL algorithms and validated via systematic ablation studies on RRT feature subsets. Compared with physicians' observed treatments, the best learned strategy reduces mortality from 13.2% to 5.0% (reducing 62.24%) and shortens average in-hospital stay from 308.5 to 250.1 hours (reducing 18.93%), demonstrating both methodological innovation and the potential of CNA-guided RL to improve RRT outcomes in nephrology.
DOI：10.1016/j.eswa.2026.133464
