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Supervised Learning for the ICD-10 Coding of French Clinical Narratives

Abstract : Automatic detection of ICD-10 codes in clinical documents has become a necessity. In this article, after a brief reminder of the existing work, we present a corpus of French clinical narratives annotated with the ICD-10 codes. Then, we propose automatic methods based on neural network approaches for the automatic detection of the ICD-10 codes. The results show that we need 1) more examples per class given the number of classes to assign, and 2) a better word/concept vector representation of documents in order to accurately assign codes.
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Contributor : Vincent Claveau Connect in order to contact the contributor
Submitted on : Tuesday, November 24, 2020 - 10:19:51 AM
Last modification on : Tuesday, November 22, 2022 - 2:26:15 PM
Long-term archiving on: : Thursday, February 25, 2021 - 7:10:11 PM


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  • HAL Id : hal-03020990, version 1


Clément Dalloux, Vincent Claveau, Marc Cuggia, Guillaume Bouzillé, Natalia Grabar. Supervised Learning for the ICD-10 Coding of French Clinical Narratives. MIE 2020 - Medical Informatics Europe conference - Digital Personalized Health and Medicine, Apr 2020, Geneva, Switzerland. pp.1-5. ⟨hal-03020990⟩



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