MedGraph：用图记忆维护长期医疗对话中的患者状态
MedGraph: Maintaining Patient State with Graph Memory for Long-Term Medical Dialogue
AI 导读
MedGraph 是一种面向长期医疗对话的图记忆方法，针对检索证据过时、混入他人健康事实、缺失安全事实三类错误，从每次会话抽取带日期的事实、归属到个人，并将用药、诊断、测量记录连成保留历史状态的链。
正文 · 原文
Abstract:LLM-based medical assistants that follow a patient across visits need a memory that keeps the patient's clinical state correct as it changes. We examine the errors of five memory systems on longitudinal medical dialogue and find three recurring causes: the retrieved evidence gives an outdated state, includes another person's health facts, or lacks a safety fact the answer depends on. We present MedGraph, a graph memory built for these three cases. Construction extracts dated facts from each session, attributes each fact to the person it describes, and links medication, diagnosis, and measurement records into chains that keep earlier states. Retrieval ranks the patient's chains as units, reads each chain at the time the question asks about, and adds the original dialogue text; a patient profile lists every recorded safety fact with every question. On MedMemoryBench, MedGraph has the highest overall accuracy with both a GPT and a Qwen backbone, 10.5 and 10.4 points above the strongest of five baselines, with efficient memory construction and answering in the GPT setting.
| Comments: | 14 pages, 5 figures, 8 tables |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.08433 [cs.CL] |
| (or arXiv:2611.08433v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08433 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Donghua Zhang [view email]
[v1]
Thu, 5 Nov 2026 18:27:20 UTC (422 KB)
来源：arXiv cs.AI · arxiv.org