Knowledge-guided generative artificial intelligence for automated taxonomy learning from drug labels.

ABSTRACT:

Objectives

To automatically construct a drug indication taxonomy from drug labels using generative Artificial Intelligence (AI) represented by the Large Language Model (LLM) GPT-4 and real-world evidence (RWE).

Materials and methods

We extracted indication terms from 46 421 free-text drug labels using GPT-4, iteratively and recursively generated indication concepts and inferred indication concept-to-concept and concept-to-term subsumption relations by integrating GPT-4 with RWE, and created a drug indication taxonomy. Quantitative and qualitative evaluations involving domain experts were performed for cardiovascular (CVD), Endocrine, and Genitourinary system diseases.

Results

2909 drug indication terms were extracted and assigned into 24 high-level indication categori

SUBMITTER: Fang Y 

PROVIDER: S-EPMC11339527 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature