High-Throughput Phenotyping of Clinical Text Using Large Language Models

Autor:	Hier, Daniel B., Munzir, S. Ilyas, Stahlfeld, Anne, Obafemi-Ajayi, Tayo, Carrithers, Michael D.
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence I.7 I.2
Druh dokumentu:	Working Paper
Popis:	High-throughput phenotyping automates the mapping of patient signs to standardized ontology concepts and is essential for precision medicine. This study evaluates the automation of phenotyping of clinical summaries from the Online Mendelian Inheritance in Man (OMIM) database using large language models. Due to their rich phenotype data, these summaries can be surrogates for physician notes. We conduct a performance comparison of GPT-4 and GPT-3.5-Turbo. Our results indicate that GPT-4 surpasses GPT-3.5-Turbo in identifying, categorizing, and normalizing signs, achieving concordance with manual annotators comparable to inter-rater agreement. Despite some limitations in sign normalization, the extensive pre-training of GPT-4 results in high performance and generalizability across several phenotyping tasks while obviating the need for manually annotated training data. Large language models are expected to be the dominant method for automating high-throughput phenotyping of clinical text. Comment: Submitted to IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Houston TX
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.01214 Zobrazit plný text záznamu View this record from Arxiv