Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking
Quick Answer
This paper shows that A new multi-agent framework integrates 466,525 Reddit posts and 60,782 WebMD reviews with FDA records, achieving F1 scores of 0.969 for medications.
Quick Take
This approach highlights the independent safety signals from patient-generated data, particularly for sertraline, where adverse events were reported much earlier than FDA records.
Key Points
- Developed a knowledge-graph-based framework for psychiatric medication safety.
- Achieved F1 scores of 0.969 for medications and 0.973 for conditions.
- Patient-generated data showed high concordance with community platforms.
- Adverse events for sertraline appeared in community sources before FDA reports.
- The Neo4j knowledge graph maintains provenance and distinguishes claims.
Paper Resources
📖 Reader Mode
~2 min readAuthors: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
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. 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 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lizhou Fan [view email]
[v1]
Wed, 24 Jun 2026 16:54:50 UTC (4,600 KB)
— Originally published at arxiv.org
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