Abstract
This manuscript presents PheNormGPT, a framework for extraction and normalization of key findings in clinical text. PheNormGPT relies on an innovative approach, leveraging large language models to extract key findings and phenotypic data in unstructured clinical text and map them to Human Phenotype Ontology concepts. It utilizes OpenAI's GPT-3.5 Turbo and GPT-4 models with fine-tuning and few-shot learning strategies, including a novel few-shot learning strategy for custom-tailored few-shot example selection per request. PheNormGPT was evaluated in the BioCreative VIII Track 3: Genetic Phenotype Extraction from Dysmorphology Physical Examination Entries shared task. PheNormGPT achieved an F1 score of 0.82 for standard matching and 0.72 for exact matching, securing first place for this shared task.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Database : the journal of biological databases and curation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.