Identifying Biomedical Entities for Datasets in Scientific Articles: 4-Step Cache-Augmented Generation Approach Using GPT-4o and PubTator 3.0.

ABSTRACT:

Background

The accurate extraction of biomedical entities in scientific articles is essential for effective metadata annotation of research datasets, ensuring data findability, accessibility, interoperability, and reusability in collaborative research.

Objective

This study aimed to introduce a novel 4-step cache-augmented generation approach to identify biomedical entities for an automated metadata annotation of datasets, leveraging GPT-4o and PubTator 3.0.

Methods

The method integrates four steps: (1) generation of candidate entities using GPT-4o, (2) validation via PubTator 3.0, (3) term extraction based on a metadata schema developed for the specific research area, and (4) a combined evaluation of PubTator-validated and schema-related terms. Applied to 23 articles published in the context of the Collaborative Research Center OncoEscape, the process was validated through supervised, face-to-face interviews with article authors, allowing an assessment of annotation precision using random-effects meta-analysis.

Results

The approach yielded a mean of 19.6 schema-related and 6.7 PubTator-validated biomedical entities per article. Within the study's specific context, the overall annotation precision was 98% (95% CI 94%-100%), with most prediction errors concentrated in articles outside the primary basic research domain of the schema. In a subsample (n=20), available supplemental material was included in the prediction process, but it did not improve precision (98%, 95% CI 95%-100%). Moreover, the mean number of schema-related entities was 20.1 (P=.56) and the mean number of PubTator-validated entities was 6.7 (P=.68); these values did not increase with the additional information provided in the supplement.

Conclusions

This study highlights the potential of large language model-supported metadata annotation. The findings underscore the practical feasibility of full-text analysis and suggest its potential for integration into routine workflows for biomedical metadata generation.

SUBMITTER: Giuliani C 

PROVIDER: S-EPMC12633840 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature