High-Throughput Phenotyping of Clinical Text Using Large Language Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908401004445696 |
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| author | Hier, Daniel B. Munzir, S. Ilyas Stahlfeld, Anne Obafemi-Ajayi, Tayo Carrithers, Michael D. |
| author_facet | Hier, Daniel B. Munzir, S. Ilyas Stahlfeld, Anne Obafemi-Ajayi, Tayo Carrithers, Michael D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01214 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | High-Throughput Phenotyping of Clinical Text Using Large Language Models Hier, Daniel B. Munzir, S. Ilyas Stahlfeld, Anne Obafemi-Ajayi, Tayo Carrithers, Michael D. Computation and Language Artificial Intelligence I.7; I.2 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. |
| title | High-Throughput Phenotyping of Clinical Text Using Large Language Models |
| topic | Computation and Language Artificial Intelligence I.7; I.2 |
| url | https://arxiv.org/abs/2408.01214 |