Tayeb Merabti

Aligning Biomedical Terminologies in French: Towards Semantic Interoperability in Medical Applications

In health, there exist practically as many different terminologies, controlled vocabularies, thesauri and classification systems as there are fields of application. In fact, terminologies play important roles in clinical data capture, annotation, reporting, information integration, indexing and retrieval. These knowledge sources havemostly different formats and purposes. For example, among many other knowledge sources, the Systematized NOmenclature of MEDicine International (SNOMED Int) is used for clinical coding, the French CCAM for procedures, the 10th revision of the International Classification of Diseases (ICD10) and the Anatomical Therapeutic Chemical (ATC) Classification for drugs are used for epidemiological and medico-economic purposes and the Medical Subject Headings (MeSH) thesaurus for indexing bibliographic databases. Given the great number of terminologies, existing tools, such as search engines, coding systems or decision support systems, are limited in dealing with “syntactic” and “semantic” divergences in spite of their great storage capacity and quick processing of data. Faced with this reality and the increasing need to allow cooperation with/between the various health actors and their related health information systems, it appears necessary to link and connect these terminologies to make them “interoperable”. The objective is to allow the different actors to speak the same language while using different representations of the same things. As it is essential to render these terminologies “interoperable”, this involves establishing a joint semantic repository to allow effective interaction with a minimum loss of meaning. This semantic interoperability requires a shared model, i.e. a common representation of terms and concepts, whatever the original terminology or repository is but it also requires the development of methods to allow connection between equivalent terms or relations from each terminology.

Multiple Terminologies in a Health Portal: Automatic Indexing and Information Retrieval

Background: In the specific context of developing quality-controlled health gateways, several standards must be respected (e.g. Dublin Core for metadata element set; thesaurus MeSH as the controlled vocabulary to index Internet resources; HON code to accredit quality of health Web sites). These standards were applied to create the CISMeF Web site (French acronym for Catalog & Index of Health Internet resources in French). Objective: In this work, the strategic shift of the CISMeF team is intended to index and retrieve French resources not anymore with a single terminology (MeSH thesaurus) but with the main health terminologies available in French (ICD 10, SNOMED International, CCAM, ATC). Methods & Results: Since 2005, we have developed the French Multi-Terminology Indexer (F-MTI), using a multi-terminology approach and mappings between health terminologies. This tool is used for automatic indexing and information retrieval. Conclusion: Since the last quarter of 2008, F-MTI is daily used in the CISMeF production environment and is connected to a French Health Multi-Terminology Server.

Projection des relations SNOMED CT entre les termes de deux terminologies (CIM10 et SNOMED 3.5)

Background The situation of medical coding and medical economics is quite specific in France. Two specific health terminologies are used: ICDIO and CCAM Another will be used in the near future SNOMED Int.

Evaluating alignment quality between iconic language and reference terminologies using similarity metrics.

BackgroundVisualization of Concepts in Medicine (VCM) is a compositional iconic language that aims to ease information retrieval in Electronic Health Records (EHR), clinical guidelines or other medical documents. Using VCM language in medical applications requires alignment with medical reference terminologies. Alignment from Medical Subject Headings (MeSH) thesaurus and International Classification of Diseases – tenth revision (ICD10) to VCM are presented here. This study aim was to evaluate alignment quality between VCM and other terminologies using different measures of inter-alignment agreement before integration in EHR.MethodsFor medical literature retrieval purposes and EHR browsing, the MeSH thesaurus and the ICD10, both organized hierarchically, were aligned to VCM language. Some MeSH to VCM alignments were performed automatically but others were performed manually and validated. ICD10 to VCM alignment was entirely manually performed. Inter-alignment agreement was assessed on ICD10 codes and MeSH descriptors, sharing the same Concept Unique Identifiers in the Unified Medical Language System (UMLS). Three metrics were used to compare two VCM icons: binary comparison, crude Dice Similarity Coefficient (DSCcrude), and semantic Dice Similarity Coefficient (DSCsemantic), based on Lin similarity. An analysis of discrepancies was performed.ResultsMeSH to VCM alignment resulted in 10,783 relations: 1,830 of which were manually performed and 8,953 were automatically inherited. ICD10 to VCM alignment led to 19,852 relations. UMLS gathered 1,887 alignments between ICD10 and MeSH. Only 1,606 of them were used for this study. Inter-alignment agreement using only validated MeSH to VCM alignment was 74.2% [70.5-78.0]CI95%, DSCcrude was 0.93 [0.91-0.94]CI95%, and DSCsemantic was 0.96 [0.95-0.96]CI95%. Discrepancy analysis revealed that even if two thirds of errors came from the reviewers, UMLS was nevertheless responsible for one third.ConclusionsThis study has shown strong overall inter-alignment agreement between MeSH to VCM and ICD10 to VCM manual alignments. VCM icons have now been integrated into a guideline search engine (http://www.cismef.org) and a health terminologies portal (http://www.hetop.eu).

Searching for rare diseases in PubMed: a blind comparison of Orphanet expert query and query based on terminological knowledge

BackgroundDespite international initiatives like Orphanet, it remains difficult to find up-to-date information about rare diseases. The aim of this study is to propose an exhaustive set of queries for PubMed based on terminological knowledge and to evaluate it versus the queries based on expertise provided by the most frequently used resource in Europe: Orphanet.MethodsFour rare disease terminologies (MeSH, OMIM, HPO and HRDO) were manually mapped to each other permitting the automatic creation of expended terminological queries for rare diseases. For 30 rare diseases, 30 citations retrieved by Orphanet expert query and/or query based on terminological knowledge were assessed for relevance by two independent reviewers unaware of the query’s origin. An adjudication procedure was used to resolve any discrepancy. Precision, relative recall and F-measure were all computed.ResultsFor each Orphanet rare disease (n = 8982), there was a corresponding terminological query, in contrast with only 2284 queries provided by Orphanet. Only 553 citations were evaluated due to queries with 0 or only a few hits. There were no significant differences between the Orpha query and terminological query in terms of precision, respectively 0.61 vs 0.52 (p = 0.13). Nevertheless, terminological queries retrieved more citations more often than Orpha queries (0.57 vs. 0.33; p = 0.01). Interestingly, Orpha queries seemed to retrieve older citations than terminological queries (p < 0.0001).ConclusionThe terminological queries proposed in this study are now currently available for all rare diseases. They may be a useful tool for both precision or recall oriented literature search.

InterSTIS: Interopérabilité sémantique de terminologies de santé francophones

Background: the proliferation of health-related documents raises issues of indexing and search in large databases. Various terminologies are then used to index documents of all types: SNOMED Int. for clinical information coding, ICD-10 and the French CCAM for medico-economic coding, ICPC used by general practitioners, the MeSH for scientific literature, etc. Objective: the goal of the project InterSTIS was to unify these terminologies and to make them interoperable within a “multisource terminology server”. Material and method: the design and developments are based on standards and norms recognized for modeling and implementation. Results: results are a meta-model for healthcare terminologies, the integration of some of them in a unique system, the mapping of terminologies by means of the UMLS Metathesaurus, and the demonstration made by the integration of a new terminology dedicated to drugs documentation, and services applied to indexing and retrieving information. Discussion: further developments may involve both the National Cancer Institute Metathesaurus and the Semantic Network of the UMLS which complete the UMLS Metathesaurus by means of explicit semantic relationships.

Un modèle de données adapté à la recherche d’information dans le dossier patient informatisé: Étude, conception et évaluation

Background: Electronic Health Records (EHRs) have an important role in medical decision making and are considered as being a major part of medical support system. To ensure this role effectively, the information must be easily found, even in voluminous and large number of EHRs. This requires to develop search capabilities for EHR information retrieval (IR). Methods: To perform this, we propose a data model adapted to IR. The data analysis of EHRs of the Rouen University Hospital led us to consider them as being a set of informational elements linked by temporal and conceptual relationships. This model consists of two main entities to manage the information elements and their metadata. After implementation, we have evaluated the capacity of the proposed model to take into account all data from the EHRs and its adaptation to IR. Results: We have performed 25 queries on EHR (20 correspond to single patient and 5 to multi-patients). The results in 19 cases (including 3 partial results) were evaluated as successful. These results confirm the ability of the model to take into account most of relevant data of EHR in IR and clearly show the differences in complexity of queries between the initial and our model of IR. Conclusion: The preliminary evaluation of the data model we have developed has shown its adaptation to IR in EHR. Nevertheless, further work on larger sets is needed to confirm this.