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Cedric Bousquet - One of the best experts on this subject based on the ideXlab platform.
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MIE - Automatic generation of MedDRA terms groupings using an ontology.
Studies in health technology and informatics, 2020Co-Authors: Gunnar Declerck, Cedric Bousquet, Mariechristine JaulentAbstract:In the context of PROTECT European project, we have developed an ontology of adverse drug reactions (OntoADR) based on the original MedDRA hierarchy and a query-based method to achieve automatic MedDRA terms groupings for improving pharmacovigilance signal detection. Those groupings were evaluated against standard handmade MedDRA groupings corresponding to first priority pharmacovigilance safety topics. Our results demonstrate that this automatic method allows catching most of the terms present in the reference groupings, and suggest that it could offer an important saving of time for the achievement of pharmacovigilance groupings. This paper describes the theoretical context of this work, the evaluation methodology, and presents the principal results.
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MedInfo - PharmARTS: Terminology Web Services for Drug Safety Data Coding and Retrieval
Studies in health technology and informatics, 2020Co-Authors: Iulian Alecu, Cedric Bousquet, Patrice Degoulet, Mariechristine JaulentAbstract:MedDRA and WHO-ART are the terminologies used to encode drug safety reports. The standardisation achieved with these terminologies facilitates: 1) The sharing of safety databases; 2) Data mining for the continuous reassessment of benefit-risk ratio at national or international level or in the pharmaceutical industry. There is some debate about the capacity of these terminologies for retrieving case reports related to similar medical conditions. We have developed a resource that allows grouping similar medical conditions more effectively than WHOART and MedDRA. We describe here a software tool facilitating the use of this terminological resource thanks to an RDF framework with support for RDF Schema inferencing and querying. This tool eases coding and data retrieval in drug safety.
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MIE - Mapping of the WHO-ART terminology on Snomed CT to improve grouping of related adverse drug reactions.
Studies in health technology and informatics, 2020Co-Authors: Iulian Alecu, Cedric Bousquet, Fleur Mougin, Mariechristine JaulentAbstract:The WHO-ART and MedDRA terminologies used for coding adverse drug reactions (ADR) do not provide formal definitions of terms. In order to improve groupings, we propose to map ADR terms to equivalent Snomed CT concepts through UMLS Metathesaurus. We performed such mappings on WHO-ART terms and can automatically classify them using a description logic definition expressing their synonymies. Our gold standard was a set of 13 MedDRA special search categories restricted to ADR terms available in WHO-ART. The overlapping of the groupings within the new structure of WHO-ART on the manually built MedDRA search categories showed a 71% success rate. We plan to improve our method in order to retrieve associative relations between WHO-ART terms.
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ITCH - Modeling Keyword Search Strategy: Analysis of Pharmacovigilance Specialists' Search of MedDRA Terms.
2020Co-Authors: Romaric Marcilly, Cedric Bousquet, Laura Douze, Sylvia PelayoAbstract:In the information retrieval task, searching and choosing keywords to form the query is crucial. The present study analyzes and describes the keywords' search strategy into a thesaurus in the field of pharmacovigilance. Two ergonomics experts shadowed 22 pharmacovigilance specialists during their daily work. They focus on the strategies for searching and choosing MedDRA terms to build pharmacovigilance queries. Interviews of four pharmacovigilance specialists completed the observations. Results highlight that, for unusual or complex searches, pharmacovigilance specialists proceed iteratively in three main phases: (i) preparation of a list of terms and of evaluation criteria, (ii) exploration of the MedDRA hierarchy and choice of a term, and (iii) evaluation of the results against the criteria. Overall, the search and the choice of keywords within a thesaurus shares similarity with the information retrieval task and is closely interwoven with the query building process. Based on the results, the paper proposes design specifications for new interfaces supporting the identification of MedDRA terms so that pharmacovigilance reports searches achieve a good level of expressiveness.
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Implementation of automated signal generation in pharmacovigilance using a knowledge-based approach.
International journal of medical informatics, 2020Co-Authors: Cedric Bousquet, Agnes Lillole Louet, Corneliu Henegar, Patrice Degoulet, Mariechristine JaulentAbstract:Automated signal generation is a growing field in pharmacovigilance that relies on data mining of huge spontaneous reporting systems for detecting unknown adverse drug reactions (ADR). Previous implementations of quantitative techniques did not take into account issues related to the medical dictionary for regulatory activities (MedDRA) terminology used for coding ADRs. MedDRA is a first generation terminology lacking formal definitions; grouping of similar medical conditions is not accurate due to taxonomic limitations. Our objective was to build a data-mining tool that improves signal detection algorithms by performing terminological reasoning on MedDRA codes described with the DAML+OIL description logic. We propose the PharmaMiner tool that implements quantitative techniques based on underlying statistical and bayesian models. It is a JAVA application displaying results in tabular format and performing terminological reasoning with the Racer inference engine. The mean frequency of drug-adverse effect associations in the French database was 2.66. Subsumption reasoning based on MedDRA taxonomical hierarchy produced a mean number of occurrence of 2.92 versus 3.63 (p < 0.001) obtained with a combined technique using subsumption and approximate matching reasoning based on the ontological structure. Semantic integration of terminological systems with data mining methods is a promising technique for improving machine learning in medical databases.
Mariechristine Jaulent - One of the best experts on this subject based on the ideXlab platform.
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MIE - Grouping pharmacovigilance terms with semantic distance.
Studies in health technology and informatics, 2020Co-Authors: Marie Dupuch, Mariechristine Jaulent, Reinhard Fescharek, Magnus Lerch, Anne Jamet, Natalia GrabarAbstract:Pharmacovigilance is the activity related to collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. Beside other methods, statistical methods are in use to detect new ADR and it was noted that their combination with groupings of terms gathering similar ADRs allows to improve the detection of new ADRs. SMQs, reference groupings in the pharmacovigilance area, are built thanks to the exploitation of the MedDRA structure. Currently SMQs are overinclusive, although they can miss several relevant terms. Moreover, several important security topics are not covered by the SMQs. The objective of this work is to propose an automatic method for the creation of groupings of terms. This method is based on exploitation of the semantic distance between MedDRA terms. Through several experiences performed, we obtain a high precision and an acceptable recall.
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MIE - Mapping of the WHO-ART terminology on Snomed CT to improve grouping of related adverse drug reactions.
Studies in health technology and informatics, 2020Co-Authors: Iulian Alecu, Cedric Bousquet, Fleur Mougin, Mariechristine JaulentAbstract:The WHO-ART and MedDRA terminologies used for coding adverse drug reactions (ADR) do not provide formal definitions of terms. In order to improve groupings, we propose to map ADR terms to equivalent Snomed CT concepts through UMLS Metathesaurus. We performed such mappings on WHO-ART terms and can automatically classify them using a description logic definition expressing their synonymies. Our gold standard was a set of 13 MedDRA special search categories restricted to ADR terms available in WHO-ART. The overlapping of the groupings within the new structure of WHO-ART on the manually built MedDRA search categories showed a 71% success rate. We plan to improve our method in order to retrieve associative relations between WHO-ART terms.
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MedInfo - PharmARTS: Terminology Web Services for Drug Safety Data Coding and Retrieval
Studies in health technology and informatics, 2020Co-Authors: Iulian Alecu, Cedric Bousquet, Patrice Degoulet, Mariechristine JaulentAbstract:MedDRA and WHO-ART are the terminologies used to encode drug safety reports. The standardisation achieved with these terminologies facilitates: 1) The sharing of safety databases; 2) Data mining for the continuous reassessment of benefit-risk ratio at national or international level or in the pharmaceutical industry. There is some debate about the capacity of these terminologies for retrieving case reports related to similar medical conditions. We have developed a resource that allows grouping similar medical conditions more effectively than WHOART and MedDRA. We describe here a software tool facilitating the use of this terminological resource thanks to an RDF framework with support for RDF Schema inferencing and querying. This tool eases coding and data retrieval in drug safety.
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Implementation of automated signal generation in pharmacovigilance using a knowledge-based approach.
International journal of medical informatics, 2020Co-Authors: Cedric Bousquet, Agnes Lillole Louet, Corneliu Henegar, Patrice Degoulet, Mariechristine JaulentAbstract:Automated signal generation is a growing field in pharmacovigilance that relies on data mining of huge spontaneous reporting systems for detecting unknown adverse drug reactions (ADR). Previous implementations of quantitative techniques did not take into account issues related to the medical dictionary for regulatory activities (MedDRA) terminology used for coding ADRs. MedDRA is a first generation terminology lacking formal definitions; grouping of similar medical conditions is not accurate due to taxonomic limitations. Our objective was to build a data-mining tool that improves signal detection algorithms by performing terminological reasoning on MedDRA codes described with the DAML+OIL description logic. We propose the PharmaMiner tool that implements quantitative techniques based on underlying statistical and bayesian models. It is a JAVA application displaying results in tabular format and performing terminological reasoning with the Racer inference engine. The mean frequency of drug-adverse effect associations in the French database was 2.66. Subsumption reasoning based on MedDRA taxonomical hierarchy produced a mean number of occurrence of 2.92 versus 3.63 (p < 0.001) obtained with a combined technique using subsumption and approximate matching reasoning based on the ontological structure. Semantic integration of terminological systems with data mining methods is a promising technique for improving machine learning in medical databases.
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MIE - Automatic generation of MedDRA terms groupings using an ontology.
Studies in health technology and informatics, 2020Co-Authors: Gunnar Declerck, Cedric Bousquet, Mariechristine JaulentAbstract:In the context of PROTECT European project, we have developed an ontology of adverse drug reactions (OntoADR) based on the original MedDRA hierarchy and a query-based method to achieve automatic MedDRA terms groupings for improving pharmacovigilance signal detection. Those groupings were evaluated against standard handmade MedDRA groupings corresponding to first priority pharmacovigilance safety topics. Our results demonstrate that this automatic method allows catching most of the terms present in the reference groupings, and suggest that it could offer an important saving of time for the achievement of pharmacovigilance groupings. This paper describes the theoretical context of this work, the evaluation methodology, and presents the principal results.
Gunnar Declerck - One of the best experts on this subject based on the ideXlab platform.
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MIE - Automatic annotation of ICD-to-MedDRA mappings with SKOS predicates.
Studies in health technology and informatics, 2020Co-Authors: Gunnar Declerck, Julien Souvignet, Jean Marie Rodrigues, Mariechristine JaulentAbstract:Robust alignments between ICD and MedDRA are essential to enable the secondary use of clinical data for pharmacovigilance research. UMLS makes available ICD-to-MedDRA mappings, but they are only poorly specified, which introduces difficulties when exploited in an automatic way. SKOS vocabulary can help achieve quality and machine-processable mappings. We have developed an algorithm based on several simple rules which annotates automatically ICD-to-MedDRA mappings with SKOS predicates. The method was tested and evaluated on a sample of ICD-10-to MedDRA mappings extracted from UMLS. The algorithm demonstrated satisfying performances, especially for skos:exactMatch properties, which suggests that automatic methods can be used to improve the quality of terminology mappings.
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MIE - Automatic generation of MedDRA terms groupings using an ontology.
Studies in health technology and informatics, 2020Co-Authors: Gunnar Declerck, Cedric Bousquet, Mariechristine JaulentAbstract:In the context of PROTECT European project, we have developed an ontology of adverse drug reactions (OntoADR) based on the original MedDRA hierarchy and a query-based method to achieve automatic MedDRA terms groupings for improving pharmacovigilance signal detection. Those groupings were evaluated against standard handmade MedDRA groupings corresponding to first priority pharmacovigilance safety topics. Our results demonstrate that this automatic method allows catching most of the terms present in the reference groupings, and suggest that it could offer an important saving of time for the achievement of pharmacovigilance groupings. This paper describes the theoretical context of this work, the evaluation methodology, and presents the principal results.
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Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties.
Frontiers in Pharmacology, 2019Co-Authors: Cedric Bousquet, Eric Sadou, Julien Souvignet, Mariechristine Jaulent, Gunnar DeclerckAbstract:Background: Formal definitions allow selecting terms (e.g., identifying all terms related to ‘Infectious disease’ using the query ‘hasCausativeAgent Organism’), and terminological reasoning (e.g., ‘Hepatitis B’ is a ‘Hepatitis’, and is an ‘Infectious disease’). However, the standard international terminology MedDRA used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA. Methods: We developed several methods that collectively allow a semi-automatic semantic enrichment of MedDRA: (1) using MedDRA-to-SNOMED CT mappings (available in the UMLS metathesaurus or other mapping resources, e.g. the MedDRA preferred term ‘Hepatitis B’ is associated to the SNOMED CT concept ‘Type B viral hepatitis’) to extract terms definitions (e.g., ‘Hepatitis B’ is associated with the following properties: hasFindingSite LiverStructure, hasAssociatedMorphology InflammationMorphology, and hasCausativeAgent HepatitisBvirus); (2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “Blood and lymphatic system disorders” is decomposed in Blood system disorders AND Lymphatic system disorders), or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “Cyclic neutropenia” leads to the property hasClinicalCourse Cyclic). Results: The UMLS Metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010, and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time. Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The Ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.
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Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties
Frontiers in Pharmacology, 2019Co-Authors: Cedric Bousquet, Eric Sadou, Julien Souvignet, Mariechristine Jaulent, Gunnar DeclerckAbstract:Background: Formal definitions allow selecting terms (e.g., identifying all terms related to “Infectious disease” using the query “has causative agent organism”) and terminological reasoning (e.g., “hepatitis B” is a “hepatitis” and is an “infectious disease”). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA. Methods: We developed several methods that collectively allow a semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System metathesaurus or other mapping resources, e.g., the MedDRA preferred term “hepatitis B” is associated to the SNOMED CT concept “type B viral hepatitis”) to extract term definitions (e.g., “hepatitis B” is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “blood and lymphatic system disorders” is decomposed in blood system disorders and lymphatic system disorders) or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “cyclic neutropenia” leads to the property has clinical course cyclic). Results: The Unified Medical Language System metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010 and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1,935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time. Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.
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semantic queries expedite MedDRA terms selection thanks to a dedicated user interface a pilot study on five medical conditions
Frontiers in Pharmacology, 2019Co-Authors: Julien Souvignet, Mariechristine Jaulent, Gunnar Declerck, Hadyl Asfari, Beatrice Trombertpaviot, Cedric BousquetAbstract:Background: Searching into the MedDRA terminology is usually limited to a hierarchical search, and/or a string search. Our objective was to compare user performances when using a new kind of user interface enabling semantic queries versus classical methods, and evaluating term selection improvement in MedDRA. Methods: We implemented a forms-based web interface: OntoADR Query Tools (OQT). It relies on OntoADR, a formal resource describing MedDRA terms using SNOMED CT concepts and corresponding semantic relations enabling terminological reasoning. We then compared time spent on five examples of medical conditions using OQT or the MedDRA web browser (MWB), and precision and recall of the term selection. Results: OQT allows the user to search in MedDRA: One may enter search criteria by selecting one semantic property from a dropdown list and one or more SNOMED CT concepts related to the range of the chosen property. The user is assisted in building his query: he can add criteria and combine them. Then, the interface displays the set of MedDRA terms matching the query. Meanwhile, on average, the time spent on OQT (about 4min30) is significantly lower (-35%; p < 0.001) than time spent on MWB (about 7min). The results of the System Usability Scale (SUS) gave a score of 62.19 for OQT (rated as good). We also demonstrated increased precision (+27%; p = 0.01) and recall (+34%; p = 0.02). Computed “performance” (correct terms found per minute) is more than 3 times better with OQT than with MWB. Discussion: This pilot study establishes the feasibility of our approach based on our initial assumption: performing MedDRA queries on the five selected medical conditions using terminological reasoning expedites term selection and improves search capabilities for pharmacovigilance end users. Evaluation with a larger number of users and medical conditions are required in order to establish if OQT is appropriate for the needs of different user profiles, and to check if conclusions can be extended to other kinds of medical conditions. The application is currently limited by the non-exhaustive coverage of MedDRA by OntoADR, but nevertheless shows good performance which encourages continuing in the same direction.
Elliot G Brown - One of the best experts on this subject based on the ideXlab platform.
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using MedDRA implications for risk management
Drug Safety, 2004Co-Authors: Elliot G BrownAbstract:: The introduction of MedDRA, the Medical Dictionary for Regulatory Activities, as a standardised terminology may have a major impact on the performance of risk management. Thus, MedDRA is likely to have an important effect on the analysis of clinical trial safety data. Review of the most commonly used terms in clinical trial tables from the labelling of ten products indicated that each adverse event could be represented by many MedDRA preferred terms; this might theoretically lead to failure to identify differences in adverse event incidence between treatment arms. Possible solutions are proposed. The use of MedDRA in spontaneous reporting systems is a regulatory requirement in some countries. Variability in modes of implementation and use of the terminology are discussed; these may impose additional limitations on any use of spontaneous data for comparative purposes. There are important differences in the ways that safety databases interface with MedDRA and uncertainty about the most appropriate way to manage version changes. The characteristics of MedDRA must be taken into account when establishing methods for signal detection and its use will affect the retrieval of similar cases as required for signal evaluation. The use of MedDRA in the periodic safety update report is discussed. The possible use of MedDRA in pharmacoepidemiology is highly relevant to risk management, and some issues are briefly outlined. With regard to communication of risk, if MedDRA is introduced into existing product labelling, care must be taken that the change itself does not cause misunderstanding; the most appropriate use of MedDRA in this regard remains to be determined. There is a need for careful evaluation of MedDRA in fulfilling its various functions in pharmacovigilance, followed by definitive regulatory guidance on its use.
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Methods and Pitfalls in Searching Drug Safety Databases Utilising the Medical Dictionary for Regulatory Activities (MedDRA)^1
Drug Safety, 2003Co-Authors: Elliot G BrownAbstract:The Medical Dictionary for Regulatory Activities (MedDRA) is a unified standard terminology for recording and reporting adverse drug event data. Its introduction is widely seen as a significant improvement on the previous situation, where a multitude of terminologies of widely varying scope and quality were in use. However, there are some complexities that may cause difficulties, and these will form the focus for this paper. Two methods of searching MedDRA-coded databases are described: searching based on term selection from all of MedDRA and searching based on terms in the safety database. There are several potential traps for the unwary in safety searches. There may be multiple locations of relevant terms within a system organ class (SOC) and lack of recognition of appropriate group terms; the user may think that group terms are more inclusive than is the case. MedDRA may distribute terms relevant to one medical condition across several primary SOCs. If the database supports the MedDRA model, it is possible to perform multiaxial searching: while this may help find terms that might have been missed, it is still necessary to consider the entire contents of the SOCs to find all relevant terms and there are many instances of incomplete secondary linkages. It is important to adjust for multiaxiality if data are presented using primary and secondary locations. Other sources for errors in searching are non-intuitive placement and the selection of terms as preferred terms (PTs) that may not be widely recognised. Some MedDRA rules could also result in errors in data retrieval if the individual is unaware of these: in particular, the lack of multiaxial linkages for the Investigations SOC, Social circumstances SOC and Surgical and medical procedures SOC and the requirement that a PT may only be present under one High Level Term (HLT) and one High Level Group Term (HLGT) within any single SOC. Special Search Categories (collections of PTs assembled from various SOCs by searching all of MedDRA) are limited by the small number available and by lack of clarity about criteria applied in their construction. Difficulties in database searching may be addressed by suitable user training and experience, and by central reporting of detected deficiencies in MedDRA. Other remedies may include regulatory guidance on implementation and use of MedDRA. Further systematic review of MedDRA is needed and generation of standardised searches that may be used ‘off the shelf’ will help, particularly where the same search is performed repeatedly on multiple data sets. Until these enhancements are widely available, MedDRA users should take great care when searching a safety database to ensure that cases are not inadvertently missed.
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methods and pitfalls in searching drug safety databases utilising the medical dictionary for regulatory activities MedDRA
Drug Safety, 2003Co-Authors: Elliot G BrownAbstract:The Medical Dictionary for Regulatory Activities (MedDRA) is a unified standard terminology for recording and reporting adverse drug event data. Its introduction is widely seen as a significant improvement on the previous situation, where a multitude of terminologies of widely varying scope and quality were in use. However, there are some complexities that may cause difficulties, and these will form the focus for this paper.
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the medical dictionary for regulatory activities MedDRA
Drug Safety, 1999Co-Authors: Elliot G Brown, Louise Wood, Sue WoodAbstract:The International Conference on Harmonisation has agreed upon the structure and content of the Medical Dictionary for Regulatory Activities (MedDRA) version 2.0 which should become available in the early part of 1999.
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Pharmacovigilance, Second Edition - The Medical Dictionary for Regulatory Activities (MedDRA)
Drug Safety, 1999Co-Authors: Elliot G Brown, Louise Wood, Sue WoodAbstract:The International Conference on Harmonisation has agreed upon the structure and content of the Medical Dictionary for Regulatory Activities (MedDRA) version 2.0 which should become available in the early part of 1999.
Julien Souvignet - One of the best experts on this subject based on the ideXlab platform.
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MIE - Automatic annotation of ICD-to-MedDRA mappings with SKOS predicates.
Studies in health technology and informatics, 2020Co-Authors: Gunnar Declerck, Julien Souvignet, Jean Marie Rodrigues, Mariechristine JaulentAbstract:Robust alignments between ICD and MedDRA are essential to enable the secondary use of clinical data for pharmacovigilance research. UMLS makes available ICD-to-MedDRA mappings, but they are only poorly specified, which introduces difficulties when exploited in an automatic way. SKOS vocabulary can help achieve quality and machine-processable mappings. We have developed an algorithm based on several simple rules which annotates automatically ICD-to-MedDRA mappings with SKOS predicates. The method was tested and evaluated on a sample of ICD-10-to MedDRA mappings extracted from UMLS. The algorithm demonstrated satisfying performances, especially for skos:exactMatch properties, which suggests that automatic methods can be used to improve the quality of terminology mappings.
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Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties.
Frontiers in Pharmacology, 2019Co-Authors: Cedric Bousquet, Eric Sadou, Julien Souvignet, Mariechristine Jaulent, Gunnar DeclerckAbstract:Background: Formal definitions allow selecting terms (e.g., identifying all terms related to ‘Infectious disease’ using the query ‘hasCausativeAgent Organism’), and terminological reasoning (e.g., ‘Hepatitis B’ is a ‘Hepatitis’, and is an ‘Infectious disease’). However, the standard international terminology MedDRA used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA. Methods: We developed several methods that collectively allow a semi-automatic semantic enrichment of MedDRA: (1) using MedDRA-to-SNOMED CT mappings (available in the UMLS metathesaurus or other mapping resources, e.g. the MedDRA preferred term ‘Hepatitis B’ is associated to the SNOMED CT concept ‘Type B viral hepatitis’) to extract terms definitions (e.g., ‘Hepatitis B’ is associated with the following properties: hasFindingSite LiverStructure, hasAssociatedMorphology InflammationMorphology, and hasCausativeAgent HepatitisBvirus); (2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “Blood and lymphatic system disorders” is decomposed in Blood system disorders AND Lymphatic system disorders), or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “Cyclic neutropenia” leads to the property hasClinicalCourse Cyclic). Results: The UMLS Metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010, and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time. Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The Ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.
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Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties
Frontiers in Pharmacology, 2019Co-Authors: Cedric Bousquet, Eric Sadou, Julien Souvignet, Mariechristine Jaulent, Gunnar DeclerckAbstract:Background: Formal definitions allow selecting terms (e.g., identifying all terms related to “Infectious disease” using the query “has causative agent organism”) and terminological reasoning (e.g., “hepatitis B” is a “hepatitis” and is an “infectious disease”). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA. Methods: We developed several methods that collectively allow a semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System metathesaurus or other mapping resources, e.g., the MedDRA preferred term “hepatitis B” is associated to the SNOMED CT concept “type B viral hepatitis”) to extract term definitions (e.g., “hepatitis B” is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “blood and lymphatic system disorders” is decomposed in blood system disorders and lymphatic system disorders) or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “cyclic neutropenia” leads to the property has clinical course cyclic). Results: The Unified Medical Language System metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010 and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1,935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time. Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.
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semantic queries expedite MedDRA terms selection thanks to a dedicated user interface a pilot study on five medical conditions
Frontiers in Pharmacology, 2019Co-Authors: Julien Souvignet, Mariechristine Jaulent, Gunnar Declerck, Hadyl Asfari, Beatrice Trombertpaviot, Cedric BousquetAbstract:Background: Searching into the MedDRA terminology is usually limited to a hierarchical search, and/or a string search. Our objective was to compare user performances when using a new kind of user interface enabling semantic queries versus classical methods, and evaluating term selection improvement in MedDRA. Methods: We implemented a forms-based web interface: OntoADR Query Tools (OQT). It relies on OntoADR, a formal resource describing MedDRA terms using SNOMED CT concepts and corresponding semantic relations enabling terminological reasoning. We then compared time spent on five examples of medical conditions using OQT or the MedDRA web browser (MWB), and precision and recall of the term selection. Results: OQT allows the user to search in MedDRA: One may enter search criteria by selecting one semantic property from a dropdown list and one or more SNOMED CT concepts related to the range of the chosen property. The user is assisted in building his query: he can add criteria and combine them. Then, the interface displays the set of MedDRA terms matching the query. Meanwhile, on average, the time spent on OQT (about 4min30) is significantly lower (-35%; p < 0.001) than time spent on MWB (about 7min). The results of the System Usability Scale (SUS) gave a score of 62.19 for OQT (rated as good). We also demonstrated increased precision (+27%; p = 0.01) and recall (+34%; p = 0.02). Computed “performance” (correct terms found per minute) is more than 3 times better with OQT than with MWB. Discussion: This pilot study establishes the feasibility of our approach based on our initial assumption: performing MedDRA queries on the five selected medical conditions using terminological reasoning expedites term selection and improves search capabilities for pharmacovigilance end users. Evaluation with a larger number of users and medical conditions are required in order to establish if OQT is appropriate for the needs of different user profiles, and to check if conclusions can be extended to other kinds of medical conditions. The application is currently limited by the non-exhaustive coverage of MedDRA by OntoADR, but nevertheless shows good performance which encourages continuing in the same direction.
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groupement automatise de termes lies aux valvulopathies medicamenteuses dans MedDRA
Therapie, 2016Co-Authors: Hadyl Asfari, Julien Souvignet, Mariechristine Jaulent, Agnes Lillole Louet, Beatrice Trombert, Cedric BousquetAbstract:Resume Objectif Proposer une methode de regroupement de termes medical dictionary for regulatory activities (MedDRA) alternative a l’approche habituelle : methode hierarchique (selection de groupements de reference dans MedDRA) et/ou methode textuelle (recherche de chaines de caracteres). Exemple des valvulopathies medicamenteuses. Methodes Liste de termes obtenue par une approche automatisee, basee sur l’interrogation d’une base de connaissances definissant les termes MedDRA au moyen de relations avec des concepts systematized nomenclature of medicine–clinical terms (SNOMED CT), comparee avec la liste de reference obtenue par methode hierarchique et textuelle. Resultats Le premier groupement automatise de termes MedDRA en rapport avec une fibrose, un retrecissement ou une calcification de valve cardiaque, excluant les pathologies congenitales et le deuxieme reprenant les memes criteres en remplacant l’aspect morphologique par l’aspect fonctionnel, presentent respectivement un rappel de 79 % avec une precision de 100 %, et un rappel de 100 % avec une precision de 96 %. Conclusion Une approche alternative aux groupements de reference MedDRA est possible pour les valvulopathies medicamenteuses et pourrait etre etendue a d’autres effets indesirables.