This paper is in the following e-collection/theme issue:

Natural Language Processing (636)

Published on in Vol 7, No 3 (2019): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12310, first published .
Word Embedding for the French Natural Language in Health Care: Comparative Study

Word Embedding for the French Natural Language in Health Care: Comparative Study

Word Embedding for the French Natural Language in Health Care: Comparative Study

Authors of this article:

Emeric Dynomant1, 2, 3 Author Orcid Image ;   Romain Lelong2, 3 Author Orcid Image ;   Badisse Dahamna2, 4 Author Orcid Image ;   Clément Massonnaud2 Author Orcid Image ;   Gaétan Kerdelhué2, 4 Author Orcid Image ;   Julien Grosjean2, 4 Author Orcid Image ;   Stéphane Canu3 Author Orcid Image ;   Stefan J Darmoni2, 4 Author Orcid Image
Article Authors Cited by (10) Tweetations (3) Metrics

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  2. Garcia-Rudolph A, Saurí J, Cegarra B, Bernabeu Guitart M. Discovering the Context of People With Disabilities: Semantic Categorization Test and Environmental Factors Mapping of Word Embeddings from Reddit. JMIR Medical Informatics 2020;8(11):e17903 View
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  5. Yokokawa D, Noda K, Yanagita Y, Uehara T, Ohira Y, Shikino K, Tsukamoto T, Ikusaka M. Validating the representation of distance between infarct diseases using word embedding. BMC Medical Informatics and Decision Making 2022;22(1) View
  6. Oubenali N, Messaoud S, Filiot A, Lamer A, Andrey P. Visualization of medical concepts represented using word embeddings: a scoping review. BMC Medical Informatics and Decision Making 2022;22(1) View
  7. Yokokawa D, Noda K, Uehara T, Yanagita Y, Ohira Y, Ikusaka M. Do Japanese word-embedded representations obtained in the academic corpus retain the medical concepts of “infarction”?. Artificial Intelligence in Medicine 2023;143:102604 View
  8. Bazoge A, Morin E, Daille B, Gourraud P. Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review. JMIR Medical Informatics 2023;11:e42477 View
  9. Beaney T, Clarke J, Salman D, Woodcock T, Majeed A, Aylin P, Barahona M. Identifying multi-resolution clusters of diseases in ten million patients with multimorbidity in primary care in England. Communications Medicine 2024;4(1) View

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