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

TL;DR

Llettuce is an open-source NLP tool that automates the translation of medical terms into standardized clinical codes using advanced language models, improving accuracy and privacy compliance over existing solutions.

Contribution

It introduces a novel NLP-based approach with large language models and fuzzy matching for medical term mapping, emphasizing local deployment and GDPR compliance.

Findings

Enhanced accuracy in medical term mapping.

Supports local deployment for data privacy.

Outperforms traditional methods in semantic nuance handling.

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.