Title :

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

Author(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]

Journal title :

Studies in Health Technology and Informatics

Abbreviated title :

Stud Health Technol Inform

Volume number :

270

Pages :

18-22

Publication date :

2020-06-27

ISSN :

1879-8365

English keyword(s) :

ICD-10
Clinical terminologies
Interoperability
Multilingual matching

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CHU Lille

Collections :

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

Submission date :

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