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

James Mitchell-White; Reza Omdivar; Benjamin Partridge; Esmond Urwin; Karthikeyan Sivakumar; Ruizhe Li; Andy Rae; Xiaoyan Wang; Theresia Mina; Tom Giles; Diego Garcia-Gil; Tim Beck; John Chambers; Grazziela Figueredo; Philip R Quinlan

arXiv · 2410.09076

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

James Mitchell-White·Reza Omdivar·Benjamin Partridge·Esmond Urwin·Karthikeyan Sivakumar·Ruizhe Li·Andy Rae·Xiaoyan Wang·Theresia Mina·Tom Giles·Diego Garcia-Gil·Tim Beck·John Chambers·Grazziela Figueredo·Philip R Quinlan

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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.

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James Mitchell-White, Reza Omdivar, Benjamin Partridge, Esmond Urwin, Karthikeyan Sivakumar, Ruizhe Li, Andy Rae, Xiaoyan Wang, Theresia Mina, Tom Giles, Diego Garcia-Gil, Tim Beck, John Chambers, Grazziela Figueredo, Philip R Quinlan. 2024-10-04. Llettuce: An Open Source Natural Language Processing Tool for the Translation of Medical Terms into Uniform Clinical Encoding. https://arxiv.org/abs/2410.09076

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