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TAXN: Translate Align Extract Normalize, a Multilingual Extraction Tool for Clinical Texts.
Neuraz, Antoine; Lerner, Ivan; Birot, Olivier; Arias, Camila; Han, Larry; Bonzel, Clara Lea; Cai, Tianxi; Huynh, Kim Tam; Coulet, Adrien.
Afiliación
  • Neuraz A; Heka Team, Inria, INSERM Centre de recherche des Cordeliers, Université Paris Cité, Paris, France.
  • Lerner I; Heka Team, Inria, INSERM Centre de recherche des Cordeliers, Université Paris Cité, Paris, France.
  • Birot O; Department of Biomedical Informatics, Hôpital Européen Georges Pompidou, Hôpital Necker-Enfants Malades, APHP, Paris, France.
  • Arias C; Heka Team, Inria, INSERM Centre de recherche des Cordeliers, Université Paris Cité, Paris, France.
  • Han L; Heka Team, Inria, INSERM Centre de recherche des Cordeliers, Université Paris Cité, Paris, France.
  • Bonzel CL; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Cai T; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Huynh KT; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Coulet A; Heka Team, Inria, INSERM Centre de recherche des Cordeliers, Université Paris Cité, Paris, France.
Stud Health Technol Inform ; 310: 649-653, 2024 Jan 25.
Article en En | MEDLINE | ID: mdl-38269889
ABSTRACT
Several studies have shown that about 80% of the medical information in an electronic health record is only available through unstructured data. Resources such as medical terminologies in languages other than English are limited and restrain the NLP tools. We propose here to leverage English based resources in other languages using a combination of translation, word alignment, entity extraction and term normalization (TAXN). We implement this extraction pipeline in an open-source library called "medkit". We demonstrate the interest of this approach through a specific use-case enriching a phenotypic dictionary for post-acute sequelae in COVID-19 (PASC). TAXN proved to be efficient to propose new synonyms of UMLS terms using a corpus of 70 articles in French with 356 terms enriched with at least one validated new synonym. This study was based on freely available deep-learning models.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Multilingüismo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Multilingüismo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Países Bajos