Assisting the translation of the CORE subset of SNOMED CT into French.

BACKGROUND: the Core Subset of SNOMED CT is part of the UMLS-Core Project dedicated to study problem list vocabularies. SNOMED CT is not yet translated into French. OBJECTIVE: to propose an automated method to assist the translation of the CORE Subset of SNOMED CT into French. MATERIAL: the 2009 AA versions of the CORE Subset of SNOMED CT and UMLS; use of four French-language terminologies integrated into the UMLS Metathesaurus: SNOMED International, ICD10, MedDRA, and MeSH. METHOD: an exact mapping completed by a close mapping between preferred terms of the CORE Subset of SNOMED CT and those of the four terminologies. RESULTS: 89% of the preferred terms of the CORE Subset of SNOMED CT are mapped with at least one preferred term in one of the four terminologies. DISCUSSION: if needed, synonymous terms could be added by the means of synonyms in the terminologies; the proposed method is independent from French and could be applied to other natural languages.

Mapping the ATC classification to the UMLS metathesaurus: some pragmatic applications.

ATC classification is a WHO international classification used to classify drugs. The aim of this paper is to evaluate two lexical methods in English and in French to map ATC to UMLS. Several applications have been impemented to illustrate the use of the ATC mapping in English and French: (a) MeSH translation in Norwegian, (b) Drug Information Portal, and (c) ATC to PubMed tool. Two lexical methods were used to map ATC to UMLS. The first approach used a French natural language processing tool to map French terms of ATC to the French terminologies of UMLS. The second approach used the MetaMap tool to map English terms of ATC to UMLS. The English MetaMap provides slightly more mappings than the French NLP tool (3,170 vs. 2,992). On the other hand, the French NLP tool provides a slightly better precision than MetaMap (88% vs. 86%). Using a manual mapping between ATC and MeSH, the union of the validated mappings between ATC and MeSH provides 2,824 mappings (68.7% of ATC codes of the fifth level). Lexical methods are powerful methods to map health terminologies to the UMLS Metathesaurus. Manual mapping is still necessary to complete the mapping.

An automated approach to map a French terminology to UMLS.

BACKGROUND: CCAM is a French terminology for coding clinical procedures. CCAM is a multi-hierarchical structured classification for procedures used in France for reimbursement in health care, which is external to UMLS. OBJECTIVE: The objective of this work is to describe a French lexical approach allowing mapping CCAM procedures to the UMLS Metathesaurus to achieve interoperability to multiple international terminologies. This approach used a preliminary step intended to take only the significant characters used to code CCAM corresponding to anatomical and actions axes. RESULTS: According to the 7,926 CCAM codes used in this study, 5,212 possible matches (exact matching, single to multiple matching, partial matching) are found using the French CCAM to UMLS based mapping, 65% of the corresponding anatomical terms in the CCAM code are mapped to at least one UMLS Concept and 37% of the corresponding action terms in the CCAM code are mapped to at least one UMLS Concept. For all the exact matches found (n=200), 91% were rated by a human expert as narrower than the mapped UMLS Concepts, while only 3% were irrelevant.

Projection and inheritance of SNOMED CT relations between MeSH terms.

This paper proposes a methodology to achieve the automatic inheritance of SNOMED CT relations applied to MeSH preferred terms using UMLS as knowledge source server. We propose an interoperability wildcard to achieve this objective. A quantitative and a qualitative analysis were performed on top four SNOMED CT relations inherited between MeSH preferred terms. A total of 12,030 couples of MeSH preferred terms are in relation via at least one SNOMED CT relationship. For the top-four relations inherited between MeSH preferred terms, overall 79.25% of them are relevant, 16.25% as intermediate and 4.5% as irrelevant, as judged by a medical librarian. This work should lead to an optimization of multi-terminology indexing tools, multi-terminology information retrieval and navigation among a multi-terminology server.

Assisting the translation of SNOMED CT into French using UMLS and four representative French-language terminologies.

OBJECTIVE: To provide a semantics-based method to assist the translation of SNOMED CT into French. To do so, we selected four French-language terminologies: ICD-10, SNOMED International, MedDRA, MeSH, as they are dedicated to different uses - epidemiology, clinical medicine, adverse reactions, medical literature, respectively - in order to map them to SNOMED Clinical Terms (CT), and thus associate French terms with SNOMED CT concepts. In this way, we measured the number of SNOMED CT concepts to be found in French-language terminologies. MATERIAL AND METHOD: We used the UMLS Metathesaurus. The mapping method was based on the coincidence of identifiers and on the explicit mappings present in the Metathesaurus. RESULTS: The study dealt exclusively with preferred terms (PTs) in the terminologies. The terminologies are mapped with varying success as regards PTs mapped to SNOMED terms (from 52% to 96%). Conversely, 45% of SNOMED CT terms are mapped by uniting the four terminologies. DISCUSSION: A more effective mapping technique than the current method is under consideration. CONCLUSION: The method presented will be refined. It could certainly provide useful assistance in the translation of SNOMED CT into French. Due to its general nature, it could be used to translate SNOMED CT into other languages than French.