Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking
This study develops a provenance-aware, knowledge-graph-based multi-agent framework that integrates Reddit posts, WebMD reviews, and FDA adverse event records for nine antidepressants, achieving high entity recognition accuracy and revealing that patient-generated data provide partly independent safety signals, with community sources often preceding regulatory reports.
[2606.26205] Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking
[Submitted on 24 Jun 2026]
Title:Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking
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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)
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