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Llettuce: An Open Source Natural Language Processing Tool for the Translation of Medical Terms into Uniform Clinical Encoding

Authors :
Mitchell-White, James
Omdivar, Reza
Urwin, Esmond
Sivakumar, Karthikeyan
Li, Ruizhe
Rae, Andy
Wang, Xiaoyan
Mina, Theresia
Chambers, John
Figueredo, Grazziela
Quinlan, Philip R
Publication Year :
2024

Abstract

This paper introduces Llettuce, an open-source tool designed to address the complexities of converting medical terms into OMOP standard concepts. Unlike existing solutions such as the Athena database search and Usagi, which struggle with semantic nuances and require substantial manual input, Llettuce leverages advanced natural language processing, including large language models and fuzzy matching, to automate and enhance the mapping process. Developed with a focus on GDPR compliance, Llettuce can be deployed locally, ensuring data protection while maintaining high performance in converting informal medical terms to standardised concepts.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2410.09076
Document Type :
Working Paper