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.
@article{arxiv.2410.09076,
title = {Llettuce: An Open Source Natural Language Processing Tool for the Translation of Medical Terms into Uniform Clinical Encoding},
author = {James Mitchell-White and Reza Omdivar and Benjamin Partridge and Esmond Urwin and Karthikeyan Sivakumar and Ruizhe Li and Andy Rae and Xiaoyan Wang and Theresia Mina and Tom Giles and Diego Garcia-Gil and Tim Beck and John Chambers and Grazziela Figueredo and Philip R Quinlan},
journal= {arXiv preprint arXiv:2410.09076},
year = {2026}
}