Large-scale identification of social and behavioral determinants of health from clinical notes: comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

These results demonstrate that the LSI approach performs comparable to more recent large language models, such as GPT-3.5 and GPT-4.0, when using the same set of documents. Importantly, LSI is robust, deterministic, and does not have document-size limitations or cost implications, which make it more amenable to real-world applications in health systems.

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Publication Details

Journal
BMC Medical Informatics and Decision Making
Published
2024-10-10
DOI
https://doi.org/10.1186/s12911-024-02705-x
Citations
4
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
1.39
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article

Large-scale identification of social and behavioral determinants of health from clinical notes: comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

Sujoy Roy, Ramin Homayouni, Lili Zhao, S. Morrell
4 citations
BMC Medical Informatics and Decision Making
Machine Learning in Healthcare
1.39
article

Large-scale identification of social and behavioral determinants of health from clinical notes: comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

Sujoy Roy, Ramin Homayouni, Lili Zhao, S. Morrell
article en
4 citations

Abstract

These results demonstrate that the LSI approach performs comparable to more recent large language models, such as GPT-3.5 and GPT-4.0, when using the same set of documents. Importantly, LSI is robust, deterministic, and does not have document-size limitations or cost implications, which make it more amenable to real-world applications in health systems.

BMC Medical Informatics and Decision MakingVol. 24(1)
Pioneer (United States) (US), Oakland University (US), Beaumont Health (US), Corewell Health
Quality Education
Openalex Percentile: Top 15%
Machine Learning in Healthcare
1.39
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