Work

Closing surveillance detection gaps for emerging digital drug discourse with ontology-enhanced large language models

Year: 2026

Type: article

Abstract: LLMs are touted as a solution to enhance digital drug surveillance but lack domain knowledge to perform these tasks effectively. Yet, when LLM architecture is enhanced by ontological frameworks, knowledge graphs, and retrieval-augmented-generation, we can evolve surveillance efforts to address a core issue contributing to surveillance lag—drug-slang and coded-language. Integrating these established methods offers a novel, linguistically grounded approach to improving the validity and responsiveness of digital drug surveillance.

Source: npj Digital Public Health

DOI: 10.1038/s44482-026-00048-0

Authors: Danny Valdez, Damir Cavar, Maria A. Parker, Xue-Jing Liu, Jon Agley

Institution: Indiana University Bloomington

Language: English

Topic: Pharmacovigilance and Adverse Drug Reactions

Subfield: Toxicology

Field: Pharmacology, Toxicology and Pharmaceutics

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7222562823