Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking

Computer Science > Artificial Intelligence [Submitted on 24 Jun 2026] Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking Huizi Yu, Jian Liu, Wenkong Wang, Lingyao Li, Jiayan Zhou, Zhaoqian Xue, Xiang Li, Xinxin Lin, Zhiying Liang, Zhuoru Wu, Siyuan Ma, Xin Ma, Lizhou Fan 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. 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. 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. 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.26205 [cs.AI]   (or arXiv:2606.26205v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.26205 Focus to learn more Submission history From: Lizhou Fan [view email] [v1] Wed, 24 Jun 2026 16:54:50 UTC (4,600 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)