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Medication safety as a use case
for argumentation mining
Jodi Schneider and Richard D. Boyce
Dagstuhl Seminar 16161
Natura...
Informatics
The management and processing of data, information
and knowledge.
2[Fourman 2002]
Informatics
The management and processing of data, information
and knowledge.
Examples:
o Biomedical informatics
o Dental ...
Evidence Informatics
The management and processing of data, information
and knowledge ABOUT evidence.
4
Evidence Informatics
The management and processing of data, information
and knowledge ABOUT evidence.
Develop end-user app...
Evidence Informatics
The management and processing of data, information
and knowledge ABOUT evidence.
Develop end-user app...
My approach to evidence informatics
o Understand user tasks and reasoning.
o Identify domain-specific argumentation scheme...
MEDICATION SAFETY
8
Prescribers check for known drug interactions.
9
Prescribers consult drug compendia which are
maintained by expert pharmacists.
Medscape EpocratesMicromedex 2.0
10
Prescribers consult drug compendia which are
maintained by expert pharmacists.
Medscape EpocratesMicromedex 2.0
11
Problem
o Thousands of preventable medication errors occur
each year.
o Clinicians rely on information in drug compendia
(...
Problem
o Drug compendia synthesize drug interaction
evidence into knowledge claims but:
• Disagree on whether specific ev...
Problem
o Drug compendia synthesize drug interaction
evidence into knowledge claims but:
• Disagree on whether specific ev...
“Addressing gaps in clinically useful
evidence on drug-drug interactions”
4-year project, U.S. National Library of Medicin...
Goals
o Long-term, provide drug compendia editors with
better information and better tools, to create the
information clin...
DOMAIN-SPECIFIC
ARGUMENTATION
17
Drug Interaction Probability Score
1. Are there previous credible reports in humans?
• If there are case reports or prospe...
19
[Hu et al. 2011] 20
21[Hu et al. 2011]
22[Hu et al. 2011]
23[Hu et al. 2011]
[Boyce, DIKB, 2006-present] 24
[Boyce, DIKB, 2006-present] 25
DESIGNING AN EVIDENCE
BASE
26
Multiple layers of evidence
Medication Safety
Studies Layer
Clinical Studies and
Experiments
Scientific Evidence Layer
27
[Brochhausen, Schneider, Malone, Empey, Hogan and Boyce “Towards a foundational representation of
potential drug-drug inte...
SCIENTIFIC EVIDENCE LAYER
29
Scientific Evidence Layer: Micropublications
[Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claim...
Scientific Evidence Layer: Micropublications
[Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claim...
MODELING NARRATIVE
DOCUMENTS AS EVIDENCE
32
33
34
MP:Claim
35
36
Building up an MP graph
37
Building up an MP graph
38
Building up an MP graph
39
Building up an MP graph
40
Building up an MP graph
41
Building up an MP graph
42
Building up an MP graph
43
Building up an MP graph
44
Building up an MP graph
45
HAND ANNOTATION TO
CREATE THE EVIDENCE BASE
46
Hand-extracting claims and evidence
o Sources
• Primary research literature
• Case reports
• FDA-approved drug labels
o Pr...
48
49
Work to date
o 410 assertions and 519 evidence items transformed
from prior work.
o 609 evidence items (pharmacokinetic po...
DIRECTIONS & FUTURE WORK
51
We are developing a search/retrieval portal
It will:
o Integrate across multiple types of source materials
(FDA drug label...
53
Evaluation plan for the search/retrieval portal
o 20-person user study
o Measures of
• Completeness of information
• Level...
Generate multiple KBs from the same EB
55
My approach to evidence informatics
o Understand user tasks and reasoning
o Identify domain-specific argumentation schemes...
Evidence modeling & curation
o Analogous processes could be used in other fields:
evidence modeling & curation is a genera...
Evidence Informatics
The management and processing of data, information
and knowledge ABOUT evidence.
Develop end-user app...
Thanks to collaborators & funders
o Training grant T15LM007059 from the National
Library of Medicine and the National Inst...
Jodi Schneider, Mathias Brochhausen, Samuel Rosko, Paolo Ciccarese,
William R. Hogan, Daniel Malone, Yifan Ning, Tim Clark...
Medication Safety Studies Layer:
DIDEO
Brochhausen et al, work in progress, example of Clinical Trial
62
DIDEO: Drug-drug Interaction and Drug-
drug Interaction Evidence Ontology
63https://github.com/DIDEO
Definitions
o Drug-drug interaction
• A biological process that results in a clinically
meaningful change to the response ...
Other Implications
o Implications for ontology development.
o Implications for improving medication safety.
65
MEDICATION SAFETY DOMAIN
66
Existing approaches: Representation
Bradford-Hill criteria (1965)
1. Strength
2. Consistency
3. Specificity
4. Temporality...
Existing approaches: Representation
Horn, J. R., Hansten, P. D., & Chan, L. N. (2007). Proposal for a new tool to evaluate...
Existing approaches: Representation
Royal Dutch Association for the Advancement of
Pharmacy (2005)
1. Existence & quality ...
Existing approaches: Representation
Boyce, DIKB, 2006-present 70
Existing approaches: Acquisition
o Evidence
71Boyce, DIKB, circa 2006
Silos: Multiple sources of information
Post-market studies
Reported in
Scientific literature
Pre-market studies Clinical e...
o What arguments are used in medication safety?
o How can these arguments be mined/identified?
o What work needs to be don...
Why is a new data model needed?
o Need computer integration
o Want a COMPUTABLE model that can make
inferences
74
Medication safety as a use case for argumentation mining, Dagstuhl seminar 16161, 2016 04-19
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Medication safety as a use case for argumentation mining, Dagstuhl seminar 16161, 2016 04-19

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Medication safety as a use case for argumentation mining

We present a use case for argumentation mining, from biomedical informatics, specifically from medication safety. Tens of thousands of preventable medical errors occur in the U.S. each year, due to limitations in the information available to clinicians. 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. The Drug Interaction Knowledge Base Project (Boyce, 2006-present; dikb.org) is addressing this problem.

Our current work is using knowledge representations and human annotation in order to represent clinically-relevant claims and evidence. Our data model incorporates an existing argumentation-focused ontology, the Micropublications Ontology. Further, to describe more specific information, such as the types of studies that allow inference of a particular type of claim, we are developing an evidence-focused ontology called DIDEO--Drug-drug Interaction and Drug-drug Interaction Evidence Ontology. On the curation side, we will describe how our research team is hand-extracting knowledge claims and evidence from the primary research literature, case reports, and FDA-approved drug labels for 65 drugs.

We think that medication safety could be an important domain for applying automatic argumentation mining in the future. In discussions at Dagstuhl, we would like to investigate how current argumentation mining techniques might be used to scale up this work. We can also discuss possible implications for representing evidence from other biomedical domains.

Talk for Dagstuhl Seminar 16161: Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments
http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=16161

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Medication safety as a use case for argumentation mining, Dagstuhl seminar 16161, 2016 04-19

  1. 1. Medication safety as a use case for argumentation mining Jodi Schneider and Richard D. Boyce Dagstuhl Seminar 16161 Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments 2016-04-19 1
  2. 2. Informatics The management and processing of data, information and knowledge. 2[Fourman 2002]
  3. 3. Informatics The management and processing of data, information and knowledge. Examples: o Biomedical informatics o Dental informatics o Legal informatics o Business informatics o Chemical informatics o Neurinformatics o ... 3[Fourman 2002]
  4. 4. Evidence Informatics The management and processing of data, information and knowledge ABOUT evidence. 4
  5. 5. Evidence Informatics The management and processing of data, information and knowledge ABOUT evidence. Develop end-user applications. e.g. Information retrieval using arguments & evidence. o Kevin’s “legal argument roles” o Benno’s PageRank for arguments o Retrieve scientific articles by rhetorical or argumentative features. 5
  6. 6. Evidence Informatics The management and processing of data, information and knowledge ABOUT evidence. Develop end-user applications. e.g. Information retrieval using arguments & evidence. o Kevin’s “legal argument roles” o Benno’s PageRank for arguments o Retrieve scientific articles by rhetorical or argumentative features. Seek reusable underlying principles, shared between several fields. 6
  7. 7. My approach to evidence informatics o Understand user tasks and reasoning. o Identify domain-specific argumentation schemes. o Fill a knowledge base with arguments. • Use domain-specific argumentation schemes as templates. • Fill “slots” in the scheme. • Hand-annotate to bootstrap information extraction. o Search engine for arguments and evidence • Use rhetorical structures. • Use argumentative structures. 7
  8. 8. MEDICATION SAFETY 8
  9. 9. Prescribers check for known drug interactions. 9
  10. 10. Prescribers consult drug compendia which are maintained by expert pharmacists. Medscape EpocratesMicromedex 2.0 10
  11. 11. Prescribers consult drug compendia which are maintained by expert pharmacists. Medscape EpocratesMicromedex 2.0 11
  12. 12. 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 12
  13. 13. 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 13
  14. 14. 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 14
  15. 15. “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 15
  16. 16. 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. 16
  17. 17. DOMAIN-SPECIFIC ARGUMENTATION 17
  18. 18. Drug Interaction Probability Score 1. Are there previous credible reports in humans? • If there are case reports or prospective studies that clearly provide evidence supporting the interaction, answer YES. For case reports, at least one case should have a “possible” DIPS rating (score of 2 or higher). • If a study appropriately designed to test for the interaction shows no evidence of an interaction, answer NO. … 5. Did the interaction remit upon de-challenge of the precipitant drug with no change in the object drug? (if no de-challenge, use Unknown or NA and skip Question 6) • Stopping the precipitant drug should bring about resolution of the interaction, even if the object drug is continued without change. … • If dechallenge of the precipitant drug without a change in object drug did not result in remission of the interaction, answer NO. • If no dechallenge occurred, the doses of both drugs were altered, or no information on dechallenge is provided, answer NA. [Horn et al. 2007] 18
  19. 19. 19
  20. 20. [Hu et al. 2011] 20
  21. 21. 21[Hu et al. 2011]
  22. 22. 22[Hu et al. 2011]
  23. 23. 23[Hu et al. 2011]
  24. 24. [Boyce, DIKB, 2006-present] 24
  25. 25. [Boyce, DIKB, 2006-present] 25
  26. 26. DESIGNING AN EVIDENCE BASE 26
  27. 27. Multiple layers of evidence Medication Safety Studies Layer Clinical Studies and Experiments Scientific Evidence Layer 27
  28. 28. [Brochhausen, Schneider, Malone, Empey, Hogan and Boyce “Towards a foundational representation of potential drug-drug interaction knowledge.” First International Workshop on Drug Interaction Knowledge Representation (DIKR-2014) at ICBO.] 28
  29. 29. SCIENTIFIC EVIDENCE LAYER 29
  30. 30. Scientific Evidence Layer: Micropublications [Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications.] 30
  31. 31. Scientific Evidence Layer: Micropublications [Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications] 31
  32. 32. MODELING NARRATIVE DOCUMENTS AS EVIDENCE 32
  33. 33. 33
  34. 34. 34
  35. 35. MP:Claim 35
  36. 36. 36
  37. 37. Building up an MP graph 37
  38. 38. Building up an MP graph 38
  39. 39. Building up an MP graph 39
  40. 40. Building up an MP graph 40
  41. 41. Building up an MP graph 41
  42. 42. Building up an MP graph 42
  43. 43. Building up an MP graph 43
  44. 44. Building up an MP graph 44
  45. 45. Building up an MP graph 45
  46. 46. HAND ANNOTATION TO CREATE THE EVIDENCE BASE 46
  47. 47. Hand-extracting claims and evidence o Sources • Primary research literature • Case reports • FDA-approved drug labels o Process • Spreadsheets • PDF annotation 47
  48. 48. 48
  49. 49. 49
  50. 50. Work to date o 410 assertions and 519 evidence items transformed from prior work. o 609 evidence items (pharmacokinetic potential drug-drug interactions) annotated by hand from 27 FDA-approved drug labels. o 230 assertions of drug-drug interactions annotated by hand from 158 non-regulatory documents, including full text research articles. 50
  51. 51. DIRECTIONS & FUTURE WORK 51
  52. 52. We are developing a search/retrieval portal It will: o Integrate across multiple types of source materials (FDA drug labels, scientific literature, …) o Systematize search: Enable ALL drug compendium editors to access the same info o Provide direct access to source materials • E.g. quotes in context 52
  53. 53. 53
  54. 54. 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 54
  55. 55. Generate multiple KBs from the same EB 55
  56. 56. My approach to evidence informatics o Understand user tasks and reasoning o Identify domain-specific argumentation schemes. o Create arguments • Use domain-specific argumentation schemes as templates. • Fill “slots” in the scheme. • Hand-annotate to bootstrap information extraction. • Automate. o Provide argument and evidence-based information retrieval • Rhetorical functions • Argumentative structures 56
  57. 57. Evidence modeling & curation o Analogous processes could be used in other fields: evidence modeling & curation is a general process. 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. 57
  58. 58. Evidence Informatics The management and processing of data, information and knowledge ABOUT evidence. Develop end-user applications such as using arguments & evidence for information retrieval. o Kevin’s “legal argument roles” o Benno’s PageRank for arguments o Retrieve scientific articles by rhetorical or argumentative features Seek reusable underlying principles, shared between several fields. 58
  59. 59. 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 59
  60. 60. 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 60
  61. 61. Medication Safety Studies Layer: DIDEO Brochhausen et al, work in progress, example of Clinical Trial 62
  62. 62. DIDEO: Drug-drug Interaction and Drug- drug Interaction Evidence Ontology 63https://github.com/DIDEO
  63. 63. 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 64
  64. 64. Other Implications o Implications for ontology development. o Implications for improving medication safety. 65
  65. 65. MEDICATION SAFETY DOMAIN 66
  66. 66. 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. 67
  67. 67. 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. 68
  68. 68. 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. 69
  69. 69. Existing approaches: Representation Boyce, DIKB, 2006-present 70
  70. 70. Existing approaches: Acquisition o Evidence 71Boyce, DIKB, circa 2006
  71. 71. Silos: Multiple sources of information Post-market studies Reported in Scientific literature Pre-market studies Clinical experience Drug product labels (US Food and Drug Administration) 72 Reported in
  72. 72. o What arguments are used in medication safety? o How can these arguments be mined/identified? o What work needs to be done?
  73. 73. Why is a new data model needed? o Need computer integration o Want a COMPUTABLE model that can make inferences 74

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