AgentEHR：基于回顾性总结的临床自主决策系统进阶

摘要

大型语言模型在医疗领域已展现出显著的应用价值，然而其在自主电子健康记录（EHR）导航中的应用仍受限于对人工筛选输入的依赖及简化的检索任务。为弥合理想化实验场景与真实临床环境间的差距，我们提出AgentEHR基准测试框架。该框架要求智能体在原始高噪声数据库中进行长程交互推理，执行诊断与治疗方案制定等复杂决策任务。研究发现，现有摘要生成方法在处理此类任务时普遍存在关键信息丢失与推理链断裂问题。为此，我们创新性提出RetroSum框架，将回溯式摘要机制与动态经验策略相融合。回溯机制通过动态重评估交互历史，有效规避长上下文信息损耗并确保逻辑连贯性；而动态经验策略则通过从记忆库中检索累积经验，弥合领域鸿沟。大量实证评估表明，RetroSum在竞争性基线模型基础上实现了最高29.16%的性能提升，同时将总体交互错误率显著降低92.3%。

English

Large Language Models have demonstrated profound utility in the medical domain. However, their application to autonomous Electronic Health Records~(EHRs) navigation remains constrained by a reliance on curated inputs and simplified retrieval tasks. To bridge the gap between idealized experimental settings and realistic clinical environments, we present AgentEHR. This benchmark challenges agents to execute complex decision-making tasks, such as diagnosis and treatment planning, requiring long-range interactive reasoning directly within raw and high-noise databases. In tackling these tasks, we identify that existing summarization methods inevitably suffer from critical information loss and fractured reasoning continuity. To address this, we propose RetroSum, a novel framework that unifies a retrospective summarization mechanism with an evolving experience strategy. By dynamically re-evaluating interaction history, the retrospective mechanism prevents long-context information loss and ensures unbroken logical coherence. Additionally, the evolving strategy bridges the domain gap by retrieving accumulated experience from a memory bank. Extensive empirical evaluations demonstrate that RetroSum achieves performance gains of up to 29.16% over competitive baselines, while significantly decreasing total interaction errors by up to 92.3%.

AgentEHR：基于回顾性总结的临床自主决策系统进阶

AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization

摘要

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