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Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
September 19, 2023 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Yikuan Li, Hanyin Wang, Halid Yerebakan, Yoshihisa Shinagawa, Yuan Luo
arXiv ID
2310.12989
Category
cs.CL: Computation & Language
Cross-listed
cs.AI
Citations
2
Venue
arXiv.org
Last Checked
5 months ago
Abstract
In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability. We leveraged the LLM to convert clinical texts into their corresponding FHIR resources. Our experiments, conducted on 3,671 snippets of clinical text, demonstrated that the LLM not only streamlines the multi-step natural language processing and human calibration processes but also achieves an exceptional accuracy rate of over 90% in exact matches when compared to human annotations.
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