using the micropublications ontology and the open annotation data model to represent evidence within...
DESCRIPTION
Presentation of a paper at the ISWC 2014 Workshop on Linked Science 2014— Making Sense Out of Data (LISC2014) - at ISWC 2014 Riva de Garda, Italy, October 19 “Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.” by Jodi Schneider, Paolo Ciccarese, Tim Clark and Richard D. Boyce. Paper: http://jodischneider.com/pubs/lisc2014.pdf Event:http://linkedscience.org/events/lisc2014/ Abstract: Semantic web technologies can support the rapid and transparent validation of scientific claims by interconnecting the assumptions and evidence used to support or challenge assertions. One important application domain is medication safety, where more efficient acquisition, representation, and synthesis of evidence about potential drug-drug interactions is needed. Exposure to potential drug-drug interactions (PDDIs), defined as two or more drugs for which an interaction is known to be possible, is a significant source of preventable drug-related harm. The combination of poor quality evidence on PDDIs, and a general lack of PDDI knowledge by prescribers, results in many thousands of preventable medication errors each year. While many sources of PDDI evidence exist to help improve prescriber knowledge, they are not concordant in their coverage, accuracy, and agreement. The goal of this project is to research and develop core components of a new model that supports more efficient acquisition, representation, and synthesis of evidence about potential drug-drug interactions. Two Semantic Web models—the Micropublications Ontology and the Open Annotation Data Model—have great potential to provide linkages from PDDI assertions to their supporting evidence: statements in source documents that mention data, materials, and methods. In this paper, we describe the context and goals of our work, propose competency questions for a dynamic PDDI evidence base, outline our new knowledge representation model for PDDIs, and discuss the challenges and potential of our approach.TRANSCRIPT
Using the Micropublications Ontology and the Open Annotation Data Model to represent evidence within
a drug-drug interaction knowledge base
Jodi Schneider, Paolo Ciccarese, Tim Clark and Richard D. Boyce
Linked Science at ISWC 2014Riva del Garda, Trentino, Italy19 October 2014
Goal of this project
Construct & maintain a knowledge base linking to evidence
i.e. data, methods, materials
where:• Each ASSERTION in the knowledge basehas a SUPPORT GRAPH of claims and evidence • Each SUPPORT GRAPH element (claims, data, methods, materials)
is dynamically linked to specific QUOTED ELEMENTS in source documents on the Web
Why? It's time-consuming to find the state of the art in a field!
• What do we know about field F? assertion X?• What evidence supports assertion X?• What assumptions are used in research
supporting assertion X?
Application domain: medication safety
• Potential drug-drug interactions– 2+ drugs, where interaction is known to be possible
• Adverse drug event– Harm caused by medication– Huge public health issue
> 1.5 million preventable adverse drug events/year (USA)
• Post-market safety issues
Drug information sources
• Evidence is selected & assessed by editorial boards– MICROMEDEX, First DataBank, Q-DIPS
• E.g. MICROMEDEX: – "In-house team of 90+ clinically-trained editorial staff"
(physicians, clinical pharmacists, nurses, medical librarians)– "Content is reviewed for clinical accuracy and relevance."– "Critical content areas may undergo an additional review by
members of our Editorial Board."• Potential problems
– a time-consuming (i.e. expensive), collaborative, process– maintaining internal and external inconsistency is non-trivial
Part of a larger effort
• “Addressing gaps in clinically useful evidence on drug-drug interactions”
• 4-year project, U.S. National Library of Medicine R01 grant (PI, Richard Boyce)
• Evidence panel of domain experts(Carol Collins, Lisa Hines, John R Horn, Phil Empey) & informaticists(Tim Clark, Paolo Ciccarese, Jodi Schneider)
• Programmer: Yifan Ning
Build on 3 things
• Drug Interaction Knowledge Base [Boyce2007, Boyce2009]
• Open Annotation Data Model [W3C2013]• Micropublications Ontology [Clark2014]
Drug Interaction Knowledge Base (DIKB)
– Hand-constructed knowledge base– Safety issues when 2 drugs are taken together– Focus is on EVIDENCE
[Boyce2007, Boyce2009]
Drug Interaction Knowledge Base (DIKB) - Boyce 2007-2009
– Hand-constructed knowledge base– Safety issues when 2 drugs are taken together– Focus is on EVIDENCE
[Boyce2007, Boyce2009]
DIKB supports queries about assertions & evidence:
• Get all assertions that are supported by a U.S. FDA regulatory guidance statement
• Are the evidence use assumptions are concordant, unique, and non-ambiguous?
• Which assertions are supported/refuted by just one type of evidence?
[Boyce2007, Boyce2009]
Evidence Entry Interface (2008)
[Boyce2007, Boyce2009]
Evidence Entry Interface (2008)
Evidence Entry Interface (2008)
Limitations of DIKB v1.2
• Cannot link quotes dynamically to source text– Document-level citation– Quote & section citation preferable
• Level of detail– Want more detail on data, methods, materials
• Minimal argumentation model– swanco:citesAsSupportingEvidence– swanco:citesAsRefutingEvidence
[Boyce2007, Boyce2009]
Open Annotation Data Model
http://www.openannotation.org/spec/core/
Micropublications Ontology (MP)
Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications
http://purl.org/mp
Goal of this project
Construct & maintain a knowledge base linking to evidence
i.e. data, methods, materials
where:• Each ASSERTION in the knowledge basehas a SUPPORT GRAPH of claims and evidence • Each SUPPORT GRAPH element (claims, data, methods, materials)
is dynamically linked to specific QUOTED ELEMENTS in source documents on the Web
Modeling strategy
Construct & maintain a knowledge base linking to evidence
i.e. data, methods, materials
where:• Each ASSERTION in the knowledge basehas a SUPPORT GRAPH of claims and evidence: MP• Each SUPPORT GRAPH element (claims, data, methods, materials)
is dynamically linked to specific QUOTED ELEMENTS in source documents on the Web
Modeling strategy
Construct & maintain a knowledge base linking to evidence
i.e. data, methods, materials
where:• Each ASSERTION in the knowledge basehas a SUPPORT GRAPH of claims and evidence: MP• Each SUPPORT GRAPH element (claims, data, methods, materials)
is dynamically linked to specific QUOTED ELEMENTS in source documents on the Web: OA
Quotes integrated (MP using OA)
http://purl.org/mp
Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications
Enhancing the DIKB with MP and OA
1. Represent the overall argument of the paper– Support & challenge relationships– Data, methods, materials
2. Semantic tagging, so drugs & proteins can be queried using knowledge from other sources
3. Make quotes actionable (highlight in orig doc)4. Handle new competency questions
Quote stored in OA, with link to source
Predicate Object
rdf:type mp:Method
rdf:value (exact text)
Predicate Object
rdf:type oa:SpecificResource
oa:hasSource <http://dailymed…>
oa:hasSelector ex:selector-1
ex:body-1 ex:target-1
ex:annotation-1
about
Quote stored in OA, with link to source
Predicate Object
rdf:type mp:Method
rdf:value (exact text)
Predicate Object
rdf:type oa:SpecificResource
oa:hasSource <http://dailymed…>
oa:hasSelector ex:selector-1
ex:body-1 ex:target-1
ex:annotation-1
about
Predicate Object
oa:prefix (preceding text)
oa:exact (exact text)
oa:postfix (following text)
ex:selector-1
New competency questions to answer
1. Finding assertions and evidence• List all assertions that are not supported by evidence
– By data, by methods, by materials
• What is the in vitro evidence for assertion X? the in vivo evidence?
– With provenance: Give me back the original data tables
2. Enabling updates• List all evidence that has been flagged as rejected from
entry into the knowledge base– By data, by methods, by materials
New competency questions to answer
3. Assessing the evidence• Which research group conducted the study used for
evidence item X?• What are the assumptions required for use of this
evidence item to support/refute assertion X? – Without directly entering them
4. Statistics for analytics/KB maintenance• Number of evidence items for and against each assertion
type– By data, by methods, by materials
Modeling challenges
• To date, MP has not been used to represent both unstructured text claims ("escitalopram does not inhibit CYP2D6") and logical representation of text as normalized subject-predicate-object (nanopublication of statement)
• Efficient querying will be needed, even when the evidence base scales. We are using an iterative design-and-test approach.
Future work
• NLP support: Create a pipeline for extracting potential drug-drug interaction (PDDI) mentions from scientific & clinical literature
• Usability tests: Tools usable by domain experts• NLP + "crowdsourcing" (distributed annotation)• Resolving links to paywalled PDFs
Acknowledgements
• Funding– ERCIM Alain Bensoussan fellowship Program
under FP7/2007-2013, grant agreement 246016– National Library of Medicine (1R01LM011838-01)
• Thanks to the Evidence Panel of Addressing PDDI Evidence Gaps: Carol Collins, Lisa Hines, and John R Horn, Phil Empey
• Thanks to programmer Yifan Ning