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Automatic generation of MedDRA terms groupings using an ontology

Automatic generation of MedDRA terms groupings using an ontology. Gunnar DECLERCK a , Cédric BOUSQUET a,b and Marie-Christine JAULENT a a INSERM, UMRS 872 EQ20, Université Paris Descartes, France. b Department of Public Health, CHU University of Saint Etienne, France.

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Automatic generation of MedDRA terms groupings using an ontology

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  1. Automatic generation of MedDRA termsgroupingsusing an ontology Gunnar DECLERCKa, Cédric BOUSQUETa,b and Marie-Christine JAULENTa aINSERM, UMRS 872 EQ20, Université Paris Descartes, France.bDepartment of Public Health, CHU University of Saint Etienne, France. MIE 2012, August 27th, Pisa

  2. Context and Rationale • MedDRA(Medical Dictionary for Drug Regulatory Activities) : standard terminology used to code adverse drugreactions (ADRs) in safety reports for postmarketingdrug surveillance. • Used for Signal detection : • Case reports coded with MedDRA stored in databases (i.e. FDA pharmacovigilance Database for the US, WHO Vigibasefor Europe) • Data mining algorithms used to find statistical correlations between ADRs (several MedDRA terms defining a unique medical condition) and drugs (a “signal”) • Empirical studies to assess the causal relationship between the drug and ADR. MedDRA hierarchical structure

  3. PROTECT WP3-SP6 “Novel techniques for grouping ADRs to improve signal detection” • Main goal: To develop and evaluate semantic driven methods for grouping MedDRA termsto improve signal detection performances. • Hypothesis: Signal detection is improved when algorithms use groupings of MedDRA terms referring to the same ADR condition (rather than one single term). • Need a way to assist human experts to build MedDRA groupings (currently made manually) • Method: Building an ADR ontology (OntoADR) providing MedDRA termswithformal machine-processabledefintions to support automatic MedDRA termsgroupingprocedures (OWL queriesselectingterms on the basis of theirsemanticproperties). PROTECT. Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium. IMI (InnovativeMedicines Initiative) project (2009- 2014).

  4. OntoADRontology • MedDRA terminology enriched with Snomed-CT concepts formal definitions • 34994 concepts (20856 MedDRA 13.0 terms / othersfrom Snomed-CT) • 26 Snomed-CT relations used to express medical meaning of MedDRA concepts HASFINDINGSITE specifies the body site affected by a condition HASOCCURRENCE refers to the specific period of life during which a condition first occurs …

  5. Ontologizing MedDRA • Hierarchical relations from SOC level to PT (Preferredterms) level are converted to subsomption (subclass_of) relations • LLT (lowlevelterms) are integrated as annotation labels of PT concepts SOC level HLGT level HLT level PT level

  6. Mappingprocess • Whenitis possible, MedDRA concepts are mappedwithSnomed-CT concepts via UMLS metathesaurus • Semantic information describing Snomed-CT concepts used to build the formaldefinition of MedDRA concepts. • When mapping impossible, formaldefinitionmade manually (via collaboration betweenknowledgeengineers and medical experts) Formaldefinition of « Dilatation intrahepaticductcongenital » Meddra PT concept afterSnomed-CT mapping (mappedwith“Congenital dilatation of lobar intrahepatic bile duct”)

  7. OntoADRgeneration: generalschema Mapping process SOC HLPT HLT PT Snomed-CT MedDRA Medical experts: Validation of mappings Manualenrichmentof OntoADR • 55.6 % of MedDRA 13.0 termscouldbedefinedusing (i) a direct mapping with a Snomed-CT concept (UMLS or other mappings methods) or (ii) a handmadedefinition. • Those terms cover 97.02 % of MedDRAtermsused in the FDA database (calculated for the period 2004-2010: 11 millions reports). = MEDDRA TERMS + FORMAL DEFINITIONS ONTOADR.owl

  8. UsingOntoADR to performautomaticquery-based MedDRA termsgroupings • Thanks to OntoADR, OWL queries can be built to select the MedDRA PTs whose formal definition fits some definitional criteria. • Example : Query to catch MedDRA terms related to “Upper gastrointestinal bleeding” • Will select from MedDRA hierarchy all PTs matching those two properties: • Duodenal ulcer haemorrhage • Gastric haemorrhage • Mallory-Weiss syndrome • Peptic ulcer haemorrhage • etc. hasAssociatedMorphology some ‘Hemorrhage’ ANDhasFindingSite some ‘Upper gastrointestinal tract structure’

  9. UsingOntoADR to performautomaticquery-based MedDRA termsgroupings • Through the subsomption mechanism, MedDRA terms referring to hemorrhages of parts of the Upper gastrointestinal tract are also selected. • ex. Oesophagealulcer haemorrhage, Gastric varices haemorrhage, etc.

  10. Evaluation of the OntoADR-basedgroupingmethod ST 1 Bullouseruptions ST 2 Acute renalfailure ST 3 Anaphylacticshock ST 4 Rhabdomyolysis ST 5 Aplasticanaemia/pancytopenia ST 6 Neutropenia ST 7 Cardiac valve fibrosis ST 8 Extrapyramidal disorders ST 9 Confusional state ST 10 Thrombocytopenia ST 11 Uppergastrointestinalbleeding ST 12 Peripheralneuropathy ST 13 Maculo-papularerythematouseruptions • Focus on 13 ADR safety topics identified by Trifiròet al (2009) as first importance pharmacovigilance targets (EU-ADR project). • For each safety topic: • Groupings of MedDRA PTs have been realized with OntoADR queries. ADR topics identified by Trifirò et al (2009) • The content of those groupings has been evaluated by comparison with existing handmade MedDRA groupings of PTs targeting same or close conditions: • Original MedDRA hierarchy groupings (HLTs or HLGTs) • SMQs (Standard Medical Queries): collections of MedDRA PTs developed manually by the MSS0 (Maintenance and Support Services Organization) to describe a common clinical condition Trifirò G, Pariente A, Coloma PM, Kors JA, Polimeni G, Miremont-Salamé G, Catania MA, Salvo F, David A, Moore N, Caputi AP, Sturkenboom M, Molokhia M, Hippisley-Cox J, Acedo CD, van der Lei J, Fourrier-Reglat A. Data mining on electronic health record databases for signal detection in pharmacovigilance: whichevents to monitor? Pharmacoepidemiol Drug Saf. 2009; 18(12):1176-84.

  11. Neutropenia Safety Topic • HLTNeutropenias • Generally defined as “an abnormallylownumber of neutrophils” (*), which are the mostabundant type of white bloodcells (leukocytes) in mammals. • Leukopenia Sometimesused as synonym of Neutropenia • But striclyspeaking Leukopenia semanticallybroader (refers to a deficit in the number of all types of white bloodcells) • To enablecomparisonwithquery-based neutropenia grouping, only neutropenia relevant PTswereselected in the SMQ • SMQ Leukopenia (*) http://www.nlm.nih.gov/medlineplus/ency/article/007230.htm

  12. Neutropenia: Building the OWL query hasDefinitionalManifestation some Neutropenia OR interprets some (hasComponent some 'Segmented neutrophil' OR hasComponent some 'Myelocyte' OR hasComponent some 'Stab form') AND (hasInterpretation some 'Below reference range' OR hasInterpretation some 'Decreased' OR hasInterpretation some 'Abnormal') (*) http://en.wikipedia.org/wiki/Neutrophil_granulocyte

  13. Neutropenia: Building the OWL query Selectingdisordersdefined by an abnormallylownumber of neutrophils (ex. autoimmune neutropenia, cyclical neutropenia, etc.) hasDefinitionalManifestation some Neutropenia OR interprets some (hasComponent some 'Segmented neutrophil' OR hasComponent some 'Myelocyte' OR hasComponent some 'Stab form') AND (hasInterpretation some 'Below reference range' OR hasInterpretation some 'Decreased' OR hasInterpretation some 'Abnormal') (*) http://en.wikipedia.org/wiki/Neutrophil_granulocyte

  14. Neutropenia: Building the OWL query hasDefinitionalManifestation some Neutropenia OR interprets some (hasComponent some 'Segmented neutrophil' OR hasComponent some 'Myelocyte' OR hasComponent some 'Stab form') AND (hasInterpretation some 'Below reference range' OR hasInterpretation some 'Decreased' OR hasInterpretation some 'Abnormal') Selecting analyses measuringquantity of neutrophils cells or neutrophils precursorcells (ex. Myelocyte count, Myelocytepercentage) Myelocyte = precursor cell in the development of the granulocyte series. Neutrophilsorneutrophil granulocytes “are generallyreferred to as either neutrophils or polymorphonuclear neutrophils (or PMNs), and are subdividedintosegmented neutrophils(or segs) and banded neutrophils[alsocalledband neutrophil or stabcell]. Theyform part of the polymorphonuclearcellfamily (PMNs) togetherwith basophils and eosinophils.” (*) (*) http://en.wikipedia.org/wiki/Neutrophil_granulocyte

  15. Neutropenia: Building the OWL query hasDefinitionalManifestation some Neutropenia OR interprets some (hasComponent some 'Segmented neutrophil' OR hasComponent some 'Myelocyte' OR hasComponent some 'Stab form') AND (hasInterpretation some 'Below reference range' OR hasInterpretation some 'Decreased' OR hasInterpretation some 'Abnormal') Selectingabnormal / lowresults of those analyses (ex. Neutrophil count abnormal, Myelocytepercentagedecreased, etc.) Myelocyte is precursor cell in the development of the granulocyte series. Neutrophilsorneutrophil granulocytes “are generallyreferred to as either neutrophils or polymorphonuclear neutrophils (or PMNs), and are subdividedintosegmented neutrophils(or segs) and banded neutrophils[alsocalledband neutrophil or stabcell]. Theyform part of the polymorphonuclearcellfamily (PMNs) togetherwith basophils and eosinophils.” (*) (*) http://en.wikipedia.org/wiki/Neutrophil_granulocyte

  16. Results: comparison with HLT • OntoADR OWL Query HLT SMQ HLT SMQ

  17. Results: comparison with HLT • OntoADR OWL Query HLT SMQ • Perfectrecall and correct precision • All PTspresent in the handmadereferencegroupingscanbeselectedautomatically HLT SMQ

  18. Bullous eruptions Safety Topic HLT Bullous conditions • OntoADR OWL Query This is not always the case! For othersafetytopicssomePTscan’tbecaughtwith the querymethod

  19. Overview of results Recall, precision and F-measure rates of automatic OntoADR groupings. SELECT (third column) indicates that a manual selection of PTs has been made in the MedDRA grouping taken as gold standard. (++) MedDRA reference groupings having a perfect semantic match with the safety topic. (+) MedDRA reference groupings having an imperfect semantic match with the safety topic (broader or narrower topic).

  20. Overview of results Our query-based grouping method allows catching most of the terms present in the MedDRA reference groupings. Recall, precision and F-measure rates of automatic OntoADR groupings. SELECT (third column) indicates that a manual selection of PTs has been made in the MedDRA grouping taken as gold standard. (++) MedDRA reference groupings having a perfect semantic match with the safety topic. (+) MedDRA reference groupings having an imperfect semantic match with the safety topic (broader or narrower topic).

  21. Overview of results Betterrecall and precision rates for HLT referencegroupingsthan for SMQ Recall, precision and F-measure rates of automatic OntoADR groupings. SELECT (third column) indicates that a manual selection of PTs has been made in the MedDRA grouping taken as gold standard. (++) MedDRA reference groupings having a perfect semantic match with the safety topic. (+) MedDRA reference groupings having an imperfect semantic match with the safety topic (broader or narrower topic).

  22. Overview of results Very good recall and correct precisionwhenconsideringonlyMeddragroupingsperfectlymatching the Safetytopics Recall, precision and F-measure rates of automatic OntoADR groupings. SELECT (third column) indicates that a manual selection of PTs has been made in the MedDRA grouping taken as gold standard. (++) MedDRA reference groupings having a perfect semantic match with the safety topic. (+) MedDRA reference groupings having an imperfect semantic match with the safety topic (broader or narrower topic).

  23. Conclusion • Results demonstrate that OntoADR and this OWL query-based grouping method can efficiently support the realization of automatic MedDRA terms groupings. • Promising result: • MedDRA terms groupings for pharmacovigilance (mainly SMQs) so far achieved manually • An automation of the process could allow an important saving of time • Next step: • (a) Some terms of the handmade reference MedDRA groupings not retrieved by our automatic queries, and (b) new terms are caught. • Does this difference in content has an impact (and if so is it positive?) on the signals detected by traditional statistical methods? • Further study to address the question of the reliability of those query-based groupings for signal detection (comparison with standard MedDRA groupings - first of all of SMQs - signal detection performances). • Forthcoming: Souvignet, J., Declerck, G., Trombert, B., Rodrigues, J.M., Jaulent, M.C., Bousquet, C. Evaluation of automatedtermgroupings for detecting anaphylactic shock signals with drugs. AMIA 2012, American Medical Informatics Association Annual Symposium, 3-7 novembre 2012, Chicago (US).

  24. ANNEX. Snomed-CT relations used in OntoADR

  25. ANNEX. Snomed-CT relations used in OntoADR

  26. ANNEX. UMLS mapping difficulties • Only about 50% of MedDRA terms are mapped with Snomed-CT concepts within UMLS. A complete mapping of MedDRA seems very difficult to realize. • Especially most MedDRA terms have no direct equivalent in Snomed-CT: their meaning must be decomposed to be mapped. • Ex.: MedDRA « Joint dislocation postoperative » |10049912| : No direct Snomed-CT equivalent • Decompositon of MedDRA term to perform the mapping: • Snomed-CT « Dislocation of joint » |108367008| • Snomed-CT « Postoperative complication » |385486001| • A single MedDRA term can be mapped to severalSnomed-CT concepts within the same UMLS concept. • UMLS embedone-to-one mappings but also one-to-several mappings. • Necessity to make manual selection to get a unique definition.

  27. ANNEX. UMLS mapping difficulties • UMLS synonymy links between MedDRA terms and Snomed-CT concepts are not always correct. • Sometimes one term is more specific than the other. • Ex.MedDRA ‘Pericardial neoplasm’ |10054945| is mapped with Snomed-CT ‘Neoplasm of uncertain behavior of pericardium’ |94997003| and ‘Neoplasm of pericardium’ |126734005| • Ex.MedDRA ‘Spondylitis’ |10061371| is mapped with Snomed-CT ‘Undifferentiated spondylitis’ |399096009|, ‘Inflammatory spondylopathy’ |202649003| and ‘Spondylitis’ |84172003| • Sometimes terms are not at all synonyms: • Ex.MedDRA ‘Vasculardisorders’ |10047065| refers to disorders of bloodandlymphaticsystems. • Snomed-CT ‘Vasculardisorder’ |27550009| onlyrefers to disorders of blood vessels (‘Disorder of lymphatic system’ isused to refer to lymphatic system troubles) • Withour mapping method, 39.8 % of MedDRA 13.0 terms (i.e. 8305 terms) weremapped via a one-to-one mapping and 9.5 % (i.e. 1984) weremapped via a one-to-severalmapping.

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