Titre :

A Semi-Automated Approach for Multilingual Terminology Matching: Mapping the French Version of the ICD-10 to the ICD-10 CM.

Auteur(s) :

Sylvestre, Emmanuelle [Auteur]
Centre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Pontchaillou]
Bouzillé, Guillaume [Auteur]
Centre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Pontchaillou]
Mcduffie, Michael [Auteur]
Harvard Medical School [Boston] [HMS]
Chazard, Emmanuel [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Avillach, Paul [Auteur]
Harvard Medical School [Boston] [HMS]
Cuggia, Marc [Auteur]
Centre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Pontchaillou]

Titre de la revue :

Studies in Health Technology and Informatics

Nom court de la revue :

Stud Health Technol Inform

Numéro :

270

Pagination :

18-22

Date de publication :

2020-06-27

ISSN :

1879-8365

Mot(s)-clé(s) en anglais :

ICD-10
Clinical terminologies
Interoperability
Multilingual matching

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

The aim of this study was to develop a simple method to map the French International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) with the International Classification of ...
Lire la suite >The aim of this study was to develop a simple method to map the French International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) with the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10 CM). We sought to map these terminologies forward (ICD-10 to ICD-10 CM) and backward (ICD-10 CM to ICD-10) and to assess the accuracy of these two mappings. We used several terminology resources such as the Unified Medical Language System (UMLS) Metathesaurus, Bioportal, the latest version available of the French ICD-10 and several official mapping files between different versions of the ICD-10. We first retrieved existing partial mapping between the ICD-10 and the ICD-10 CM. Then, we automatically matched the ICD-10 with the ICD-10-CM, using our different reference mapping files. Finally, we used manual review and natural language processing (NLP) to match labels between the two terminologies. We assessed the accuracy of both methods with a manual review of a random dataset from the results files. The overall matching was between 94.2 and 100%. The backward mapping was better than the forward one, especially regarding exact matches. In both cases, the NLP step was highly accurate. When there are no available experts from the ontology or NLP fields for multi-lingual ontology matching, this simple approach enables secondary reuse of Electronic Health Records (EHR) and billing data for research purposes in an international context.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CHU Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2023-11-15T08:40:41Z
2024-01-09T10:10:01Z