Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking

知识增强型智能体 AI：用于心理健康药物信息检索

Abstract: Patients increasingly seek medication information online, yet safety knowledge for psychiatric drugs is split between regulatory adverse-event records, which are authoritative but abstract, and patient narratives, which are experience-near but unvalidated. Integrating them without conflating evidence and anecdote is especially consequential in psychiatry, where poorly contextualised information can amplify fear, nocebo responses, and non-adherence.

摘要： 患者越来越多地在网上寻求药物信息，然而精神科药物的安全性知识被割裂为两部分：一部分是监管机构的不良事件记录，它们权威但抽象；另一部分是患者的叙述，它们贴近真实体验但未经证实。在精神病学领域，将两者整合且不混淆证据与轶事显得尤为重要，因为缺乏背景信息的药物资讯可能会加剧患者的恐惧、反安慰剂效应（nocebo responses）以及用药依从性问题。

Here we develop a provenance-aware, knowledge-graph-based multi-agent framework unifying 466,525 Reddit posts, 60,782 WebMD reviews, and twenty years of U.S. FDA Adverse Event Reporting System records for nine antidepressants. A large-language-model entity-recognition pipeline benchmarked against physician annotations reached highest F1 scores of 0.969 for medications and 0.973 for conditions.

在此，我们开发了一个具备溯源能力的、基于知识图谱的多智能体框架，整合了 466,525 条 Reddit 帖子、60,782 条 WebMD 评论，以及美国食品药品监督管理局（FDA）针对九种抗抑郁药物长达二十年的不良事件报告系统记录。通过与医生标注结果进行基准测试，该大语言模型实体识别流水线在药物识别上达到了 0.969 的最高 F1 分数，在病症识别上达到了 0.973。

The two community platforms were far more concordant with each other (overlap up to a Jaccard similarity of 0.905) than with regulatory reports, indicating that patient-generated data form a partly independent safety signal. For sertraline, many adverse events appeared in community sources hundreds of days before the corresponding FDA date.

这两个社区平台之间的数据一致性（重叠度 Jaccard 相似度高达 0.905）远高于它们与监管报告之间的一致性，这表明患者生成的数据构成了一个部分独立的安全性信号。以舍曲林（sertraline）为例，许多不良事件在社区来源中出现的时间比 FDA 对应的记录日期早了数百天。

A Neo4j knowledge graph grounded in ATC-N, ICD-10, and MedDRA vocabularies preserves provenance, keeping every claim traceable and regulatory facts distinct from patient experience. These results establish source-aware integration as a route to more auditable psychiatric medication information, with usefulness and patient benefit to be tested prospectively.

一个基于 ATC-N、ICD-10 和 MedDRA 词汇表的 Neo4j 知识图谱保留了数据溯源，确保每一项声明均可追溯，并将监管事实与患者体验区分开来。这些结果确立了“来源感知型整合”作为获取更具可审计性的精神科药物信息的一种途径，其有效性和对患者的益处将在前瞻性研究中进一步验证。