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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.09076 |
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| _version_ | 1866910049841971200 |
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| author | Mitchell-White, James Omdivar, Reza Partridge, Benjamin Urwin, Esmond Sivakumar, Karthikeyan Li, Ruizhe Rae, Andy Wang, Xiaoyan Mina, Theresia Giles, Tom Garcia-Gil, Diego Beck, Tim Chambers, John Figueredo, Grazziela Quinlan, Philip R |
| author_facet | Mitchell-White, James Omdivar, Reza Partridge, Benjamin Urwin, Esmond Sivakumar, Karthikeyan Li, Ruizhe Rae, Andy Wang, Xiaoyan Mina, Theresia Giles, Tom Garcia-Gil, Diego Beck, Tim Chambers, John Figueredo, Grazziela Quinlan, Philip R |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09076 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Llettuce: An Open Source Natural Language Processing Tool for the Translation of Medical Terms into Uniform Clinical Encoding Mitchell-White, James Omdivar, Reza Partridge, Benjamin Urwin, Esmond Sivakumar, Karthikeyan Li, Ruizhe Rae, Andy Wang, Xiaoyan Mina, Theresia Giles, Tom Garcia-Gil, Diego Beck, Tim Chambers, John Figueredo, Grazziela Quinlan, Philip R Computation and Language 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. |
| title | Llettuce: An Open Source Natural Language Processing Tool for the Translation of Medical Terms into Uniform Clinical Encoding |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.09076 |