# Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking

arXiv: https://arxiv.org/abs/2606.26205 · HTML: https://mlsanity.com/paper/2606.26205/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 5 of 5)

- [Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education](https://mlsanity.com/paper/2409.18924/) (2024)
- [Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis](https://mlsanity.com/paper/2403.16303/) (2024)
- [DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services](https://mlsanity.com/paper/2510.21228/) (2025)
- [AIPatient Arena: EHR-grounded evaluation of large language models in end-to-end clinical consultation workflows](https://mlsanity.com/paper/2606.17474/) (2026)
- [An evidence-guided reinforcement learning method to improve psychiatric reasoning in small language models](https://mlsanity.com/paper/2602.06449/) (2026)

## Abstract (arXiv)

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.