Llettuce: An Open Source Natural Language Processing Tool for the Translation of Medical Terms into Uniform Clinical Encoding
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Abstract (arXiv)
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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References · 8
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,848
- 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 861
- 2023Better Zero-Shot Reasoning with Role-Play Promptingcited by 223
- 2023Large language models in healthcare and medical domain: A reviewcited by 127
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