Sepsis remains one of the leading causes of mortality in intensive care units, where timely and accurate treatment decisions can significantly impact patient outcomes. In this work, we propose an interpretable decision support framework. Our system integrates four core components: (1) a clustering-based stratification module that categorizes patients into low, intermediate, and high-risk groups upon ICU admission, using clustering with statistical validation; (2) a synthetic data augmentation pipeline leveraging variational autoencoders (VAE) and diffusion models to enrich underrepresented trajectories such as fluid or vasopressor administration; (3) an offline reinforcement learning (RL) agent trained using Advantage Weighted Regression (AWR) with a lightweight attention encoder and supported by an ensemble models for conservative, safety-aware treatment recommendations; and (4) a rationale generation module powered by a multi-modal large language model (LLM), which produces natural-language justifications grounded in clinical context and retrieved expert knowledge. Evaluated on the MIMIC-III and eICU datasets, our approach achieves high treatment accuracy while providing clinicians with interpretable and robust policy recommendations.

翻译：脓毒症仍是重症监护病房的主要致死原因之一，及时准确的治疗决策能显著影响患者预后。本研究提出一种可解释的决策支持框架。系统整合四个核心模块：（1）基于聚类的分层模块，通过统计验证的聚类方法在患者入住ICU时将其划分为低危、中危和高危群体；（2）利用变分自编码器（VAE）和扩散模型的合成数据增强流程，用于扩充液体输注或血管加压药使用等代表性不足的治疗轨迹；（3）采用优势加权回归（AWR）训练的离线强化学习（RL）智能体，配备轻量级注意力编码器，并集成保守型安全感知治疗推荐模型；（4）由多模态大语言模型（LLM）驱动的原理生成模块，基于临床语境和检索的专家知识生成自然语言诊疗依据。在MIMIC-III和eICU数据集上的评估表明，该方法在实现高治疗准确率的同时，能为临床医生提供可解释且稳健的决策建议。