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--- |
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license: other |
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license_name: ihtsdo-and-nlm-licences |
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license_link: https://www.nlm.nih.gov/databases/umls.html |
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language: |
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- nl |
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- en |
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library_name: sentence-transformers |
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tags: |
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- medical |
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- biology |
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pipeline_tag: sentence-similarity |
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widget: |
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- source_sentence: bartonellosis |
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sentences: |
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- kattenkrabziekte |
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- wond, kattenkrab |
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- door teken overgedragen orbiviruskoorts |
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- kattenbont |
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--- |
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# In-Context Dutch Clinical Embeddings with BioLORD & MedMentions |
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Do mentions sharing the same text need to have the same embedding? No! |
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This model supports embedding biomedical entities in both English and Dutch, but support in-context embedding of concepts, using the following template: |
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``` |
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mention text [SEP] (context: ... a textual example containing mention text and some more text on both sides ...) |
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``` |
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It also supports embedding mentions without context, particularly in English. |
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## References |
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### 📖 BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights |
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Journal of the American Medical Informatics Association, 2024<br> |
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François Remy, Kris Demuynck, Thomas Demeester<br> |
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[view online](https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocae029/7614965) |
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### 📖 Annotation-preserving machine translation of English corpora to validate Dutch clinical concept extraction tools |
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Under review, with a preprint available on Medrxiv.org, 2024<br> |
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Tom Seinen, Jan Kors, Erik van Mulligen, Peter Rijnbeek<br> |
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[view online](https://www.medrxiv.org/content/medrxiv/early/2024/03/15/2024.03.14.24304289.full.pdf) |
