Utility of word embeddings from large language models in medical diagnosis

The ensemble mean of symptom embeddings improves classification accuracy over the eponymic condition approach. Future efforts should focus on medical-specific training of LLMs to enhance their diagnostic accuracy and clinical utility.

Authors

Ronald C. Henry (ORCID: https://orcid.org/0000-0002-7691-228X)
Shahram Yazdani (ORCID: https://orcid.org/0000-0002-1138-1756)
A.P. Byrne (ORCID: https://orcid.org/0000-0002-7096-6455)
Isaac Henry

Institutions

University of Southern California (US)
University of California, Los Angeles (US)
Trios Health (US)
University of San Francisco (US)

Publication Details

Journal: Journal of the American Medical Informatics Association
Published: 2025-01-09
DOI: https://doi.org/10.1093/jamia/ocae314
Citations: 1
Primary Topic: Immunodeficiency and Autoimmune Disorders
Type: article
Field-Weighted Citation Impact: 0.80

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article

Utility of word embeddings from large language models in medical diagnosis

Ronald C. Henry, Shahram Yazdani, A.P. Byrne, Isaac Henry

Jan 9, 2025

1 citations

Journal of the American Medical Informatics Association

Immunodeficiency and Autoimmune Disorders

0.80

article

Utility of word embeddings from large language models in medical diagnosis

Ronald C. Henry, Shahram Yazdani, A.P. Byrne, Isaac Henry

January 9, 2025article en

1 citations

Abstract

The ensemble mean of symptom embeddings improves classification accuracy over the eponymic condition approach. Future efforts should focus on medical-specific training of LLMs to enhance their diagnostic accuracy and clinical utility.

Journal of the American Medical Informatics AssociationVol. 32(3)

University of Southern California (US), University of California, Los Angeles (US), Trios Health (US), University of San Francisco (US)

Openalex Percentile: Top 32%

Immunodeficiency and Autoimmune Disorders

0.80

DOI Open Access PDF OpenAlex

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