An evaluation of GPT models for phenotype concept recognition.

ABSTRACT:

Objective

Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation.

Materials and methods

Th

SUBMITTER: Groza T 

PROVIDER: S-EPMC10829255 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature