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Acquiring and representing drug-drug interaction knowledge and evidence, eResearch Roundtable at GSLIS at UIUC, 2016-04-06

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Presentation at the eResearch Roundtable at GSLIS at UIUC, April 6, 2016
http://www.lis.illinois.edu/events/2016/04/06/errt-jodi-schneider

Abstract:
Limitations in the information available to clinicians are a contributing factor to the many thousands of preventable medication errors that occur each year. Current knowledge sources about potential drug-drug interactions (PDDIs) often fail to provide essential management recommendations and differ significantly in their coverage, accuracy, and agreement. To address this, Schneider and her colleagues seek to more efficiently acquire and represent PDDIs knowledge claims and their supporting evidence in a standard computable format.

In this talk Schneider will present work in progress on both representation (a data model) and acquisition (an evidence curation pipeline). The data model has a reusable generic layer, provided by the Micropublications Ontology, as well as a domain-specific layer represented using the new Drug-drug Interaction and Drug-drug Interaction Evidence Ontology (DIDEO). She will discuss the motivation for this approach and possible implications for representing evidence from other biomedical domains. On the curation side, she will describe how the research team is hand-extracting knowledge claims and evidence from the primary research literature, case reports, and FDA-approved drug labels. This work has implications for ontology development, the design of curation pipelines, and improving medication safety.

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Acquiring and representing drug-drug interaction knowledge and evidence, eResearch Roundtable at GSLIS at UIUC, 2016-04-06

  1. 1. Acquiring and representing drug- drug interaction knowledge and evidence​ Jodi Schneider eResearch Roundtable, GSLIS, UIUC 2016-04-06
  2. 2. Prescribers check for known drug interactions. 2
  3. 3. Prescribers consult drug compendia which are maintained by expert pharmacists. Medscape EpocratesMicromedex 2.0 3
  4. 4. Prescribers consult drug compendia which are maintained by expert pharmacists. Medscape EpocratesMicromedex 2.0 4
  5. 5. Problem o Thousands of preventable medication errors occur each year. o Clinicians rely on information in drug compendia (Physician’s Desk Reference, Medscape, Micromedex, Epocrates, …). o Compendia have information quality problems: • differ significantly in their coverage, accuracy, and agreement • often fail to provide essential management recommendations about prescription drugs 5
  6. 6. Problem o Drug compendia synthesize drug interaction evidence into knowledge claims but: • Disagree on whether specific evidence items can support or refute particular knowledge claims 6
  7. 7. Problem o Drug compendia synthesize drug interaction evidence into knowledge claims but: • Disagree on whether specific evidence items can support or refute particular knowledge claims • May fail to include important evidence 7
  8. 8. Silos: Multiple sources of information Post-market studies Reported in Scientific literature Reported in Pre-market studies Clinical experience Drug product labels (US Food and Drug Administration) 8
  9. 9. Goals o Long-term, provide drug compendia editors with better information and better tools, to create the information clinicians use. o This talk focuses on how we might efficiently acquire and represent • knowledge claims about medication safety • and their supporting evidence o In a standard computable format.
  10. 10. MEDICATION SAFETY DOMAIN 10
  11. 11. Definitions o Drug-drug interaction • A biological process that results in a clinically meaningful change to the response of at least one co- administrated drug. o Potential drug-drug interaction • POSSIBILITY of a drug-drug interaction • Data from a clinical/physiological study OR reasonable extrapolation about drug-drug interaction mechanisms 11
  12. 12. Existing approaches: Representation Bradford-Hill criteria (1965) 1. Strength 2. Consistency 3. Specificity 4. Temporality 5. Biological gradient 6. Plausibility 7. Coherence Bradford-Hill A. The Environment and Disease: Association or Causation?. Proc R Soc Med. 1965;58:295-300. 12
  13. 13. Existing approaches: Representation Horn, J. R., Hansten, P. D., & Chan, L. N. (2007). Proposal for a new tool to evaluate drug interaction cases. Annals of Pharmacotherapy, 41(4), 674-680. 13
  14. 14. Existing approaches: Representation Royal Dutch Association for the Advancement of Pharmacy (2005) 1. Existence & quality of evidence on the interaction 2. Clinical relevance of the potential adverse reaction resulting from the interaction 3. Risk factors identifying patient, medication or disease characteristics for which the interaction is of special importance 4. The incidence of the adverse reaction Van Roon, E.N. et al: Clinical relevance of drug-drug interactions: a structured assessment procedure. Drug Saf. 2005;28(12):1131-9. 14
  15. 15. Existing approaches: Representation Boyce, DIKB, 2006-present 15
  16. 16. Existing approaches: Acquisition o Evidence 16Boyce, DIKB, circa 2006
  17. 17. DATA MODEL: REPESENTING KNOWLEDGE
  18. 18. Why is a new data model needed? o Need computer integration o Want a COMPUTABLE model that can make inferences 18
  19. 19. Multiple layers of evidence Medication Safety Studies Layer Clinical Studies and Experiments Scientific Evidence Layer 19
  20. 20. Scientific Evidence Layer: Micropublications 20 Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications
  21. 21. MP:Claim 21
  22. 22. 22
  23. 23. Building up an MP graph 23
  24. 24. Building up an MP graph 24
  25. 25. Building up an MP graph 25
  26. 26. Building up an MP graph 26
  27. 27. Building up an MP graph 27
  28. 28. Building up an MP graph 28
  29. 29. Building up an MP graph 29
  30. 30. Building up an MP graph 30
  31. 31. Building up an MP graph 31
  32. 32. Medication Safety Studies Layer: DIDEO Brochhausen et al, work in progress, example of Clinical Trial 32
  33. 33. DIDEO: Drug-drug Interaction and Drug- drug Interaction Evidence Ontology 33https://github.com/DIDEO
  34. 34. EVIDENCE CURATION: ACQUIRING KNOWLEDGE 34
  35. 35. Hand-extracting knowledge claims and evidence o Sources • Primary research literature • Case reports • FDA-approved drug labels o Process • Spreadsheets • PDF annotation 35
  36. 36. 36
  37. 37. 37
  38. 38. 38
  39. 39. DIRECTIONS & FUTURE WORK 39
  40. 40. We are developing a search/retrieval portal It will: o Integrate information (removing silos) o Offer the same information to all compendium editors o Provide direct access to information • E.g. quotes in context
  41. 41. 41
  42. 42. Evaluation plan for the search/retrieval portal o 20-person user study o Measures of • Completeness of information • Level of agreement • Time required • Perceived ease of use 42
  43. 43. Implications for evidence modeling & curation o Evidence modeling & curation is a general process. o Analogous processes could be used in other fields. o Biomedical curation is most mature: structured nature of the evidence interpretation, existing ontologies, trained curators, information extraction and natural language processing pipelines o Curation pipelines need to be designed with stakeholders in mind. 43
  44. 44. Other Implications o Implications for ontology development. o Implications for improving medication safety. 44
  45. 45. Thanks to collaborators & funders o Training grant T15LM007059 from the National Library of Medicine and the National Institute of Dental and Craniofacial Research o The entire “Addressing gaps in clinically useful evidence on drug-drug interactions” team from U.S. National Library of Medicine R01 grant (PI, Richard Boyce; R01LM011838) and other collaborators 45
  46. 46. “Addressing gaps in clinically useful evidence on drug-drug interactions” 4-year project, U.S. National Library of Medicine R01 grant (PI, Richard Boyce; R01LM011838) o Evidence panel of domain experts: Carol Collins, Amy Grizzle, Lisa Hines, John R Horn, Phil Empey, Dan Malone o Informaticists: Jodi Schneider, Harry Hochheiser, Katrina Romagnoli, Samuel Rosko o Ontologists: Mathias Brochhausen, Bill Hogan o Programmers: Yifan Ning, Wen Zhang, Louisa Zhang 46
  47. 47. Jodi Schneider, Mathias Brochhausen, Samuel Rosko, Paolo Ciccarese, William R. Hogan, Daniel Malone, Yifan Ning, Tim Clark and Richard D. Boyce. “Formalizing knowledge and evidence about potential drug-drug interactions.” International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I 2015) at ISWC 2015 Bethlehem, Pennsylvania, USA. Jodi Schneider, Paolo Ciccarese, Tim Clark and Richard D. Boyce. “Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.” 4th Workshop on Linked Science 2014—Making Sense Out of Data (LISC2014) at ISWC 2014 Riva de Garda, Italy. Mathias Brochhausen, Jodi Schneider, Daniel Malone, Philip E. Empey, William R. Hogan and Richard D. Boyce “Towards a foundational representation of potential drug-drug interaction knowledge.” First International Workshop on Drug Interaction Knowledge Representation (DIKR-2014) at the International Conference on Biomedical Ontologies (ICBO 2014) Houston, Texas, USA. Richard D. Boyce, John Horn, Oktie Hassanzadeh, Anita de Waard, Jodi Schneider, Joanne S. Luciano, Majid Rastegar-Mojarad, Maria Liakata, “Dynamic Enhancement of Drug Product Labels to Support Drug Safety, Efficacy, and Effectiveness.” Journal of Biomedical Semantics. 4(5), 2013. doi:10.1186/2041-1480-4-5 47

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