Efficient extraction of medication information from clinical notes: an evaluation in 2 languages.
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| Názov: | Efficient extraction of medication information from clinical notes: an evaluation in 2 languages. |
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| Autori: | Fabacher T; Service de Santé Publique, University Hospital of Strasbourg, Strasbourg, 67000, France.; IMAGeS, ICube Laboratory, Université de Strasbourg, CNRS, UMR 7357, Strasbourg, 67000, France.; Inria, Inserm, Université Paris Cité, Paris, 75013, France., Sauleau EA; Service de Santé Publique, University Hospital of Strasbourg, Strasbourg, 67000, France.; IMAGeS, ICube Laboratory, Université de Strasbourg, CNRS, UMR 7357, Strasbourg, 67000, France., Arcay E; Service de Santé Publique, University Hospital of Strasbourg, Strasbourg, 67000, France., Faye B; Service de Santé Publique, University Hospital of Strasbourg, Strasbourg, 67000, France., Alter M; Service de Santé Publique, University Hospital of Strasbourg, Strasbourg, 67000, France., Chahard A; Service de Santé Publique, University Hospital of Strasbourg, Strasbourg, 67000, France., Miraillet N; Service de Santé Publique, University Hospital of Strasbourg, Strasbourg, 67000, France., Coulet A; Inria, Inserm, Université Paris Cité, Paris, 75013, France., Névéol A; CNRS, LISN, Université Paris-Saclay, Orsay, 91405, France. |
| Zdroj: | Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2025 Dec 01; Vol. 32 (12), pp. 1855-1864. |
| Spôsob vydávania: | Journal Article; Evaluation Study |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Oxford University Press Country of Publication: England NLM ID: 9430800 Publication Model: Print Cited Medium: Internet ISSN: 1527-974X (Electronic) Linking ISSN: 10675027 NLM ISO Abbreviation: J Am Med Inform Assoc Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2015- : Oxford : Oxford University Press Original Publication: Philadelphia, PA : Hanley & Belfus, c1993- |
| Výrazy zo slovníka MeSH: | Natural Language Processing* , Electronic Health Records* , Information Storage and Retrieval*/methods, Humans ; Language |
| Abstrakt: | Objective: To evaluate the accuracy, computational cost, and portability of a new natural language processing (NLP) method for extracting medication information from clinical narratives. Materials and Methods: We propose an original transformer-based architecture for the extraction of entities and their relations pertaining to patients' medication regimen. First, we used this approach to train and evaluate a model on French clinical notes, using a newly annotated corpus from Hôpitaux Universitaires de Strasbourg. Second, the portability of the approach was assessed by conducting an evaluation on clinical documents in English from the 2018 n2c2 shared task. Information extraction accuracy and computational cost were assessed by comparison with an available method using transformers. Results: The proposed architecture achieves on the task of relation extraction itself performance that are competitive with the state-of-the-art on both French and English (F-measures 0.82 and 0.96 vs 0.81 and 0.95), but reduces the computational cost by 10. End-to-end (Named Entity recognition and Relation Extraction) F1 performance is 0.69 and 0.82 for French and English corpus. Discussion: While an existing system developed for English notes was deployed in a French hospital setting with reasonable effort, we found that an alternative architecture offered end-to-end drug information extraction with comparable extraction performance and lower computational impact for both French and English clinical text processing, respectively. Conclusion: The proposed architecture can be used to extract medication information from clinical text with high performance and low computational cost and consequently suits with usually limited hospital IT resources. (© The Author(s) 2025. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.) |
| Contributed Indexing: | Keywords: French; drug prescriptions; electronic health records; natural language processing; relation extraction |
| Entry Date(s): | Date Created: 20251010 Date Completed: 20251125 Latest Revision: 20251127 |
| Update Code: | 20251127 |
| PubMed Central ID: | PMC12646380 |
| DOI: | 10.1093/jamia/ocaf113 |
| PMID: | 41071911 |
| Databáza: | MEDLINE |
Nájsť tento článok vo Web of Science | Abstrakt: | Objective: To evaluate the accuracy, computational cost, and portability of a new natural language processing (NLP) method for extracting medication information from clinical narratives.<br />Materials and Methods: We propose an original transformer-based architecture for the extraction of entities and their relations pertaining to patients' medication regimen. First, we used this approach to train and evaluate a model on French clinical notes, using a newly annotated corpus from Hôpitaux Universitaires de Strasbourg. Second, the portability of the approach was assessed by conducting an evaluation on clinical documents in English from the 2018 n2c2 shared task. Information extraction accuracy and computational cost were assessed by comparison with an available method using transformers.<br />Results: The proposed architecture achieves on the task of relation extraction itself performance that are competitive with the state-of-the-art on both French and English (F-measures 0.82 and 0.96 vs 0.81 and 0.95), but reduces the computational cost by 10. End-to-end (Named Entity recognition and Relation Extraction) F1 performance is 0.69 and 0.82 for French and English corpus.<br />Discussion: While an existing system developed for English notes was deployed in a French hospital setting with reasonable effort, we found that an alternative architecture offered end-to-end drug information extraction with comparable extraction performance and lower computational impact for both French and English clinical text processing, respectively.<br />Conclusion: The proposed architecture can be used to extract medication information from clinical text with high performance and low computational cost and consequently suits with usually limited hospital IT resources.<br /> (© The Author(s) 2025. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.) |
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| ISSN: | 1527-974X |
| DOI: | 10.1093/jamia/ocaf113 |