linked open data for medical guidelines interactions
TRANSCRIPT
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@AIME, Pavia, 19th June 2015
Linked Open Data for Medical Guidelines Interactions
Veruska Zamborlini, Marcos da Silveira, Cedric Pruski, Annette ten Teije and Frank van Harmelen, Rinke Hoekstra
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Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study (2012)
Karen Barnett, Stewart W Mercer, Michael Norbury, Prof Graham Watt, Prof Sally Wyke, Bruce Guthrie
Project Smart WardExpectation:
multi-morbidity holds for psychiatric patients as well
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In practice:✤ Paper-based Clinical Guideline
(CG)
✤ One guideline per disease
✤ Common co-morbidities (2) are addressed during CG development
✤ Not suitable for detecting interactions
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What about…✤ Computer-interpretable
Clinical Guideline (CIG)✤ How to combine CIGs?
✤ CIG Languages: ✤ mainly designed for execution✤ not suitable for detecting
interactions
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What do we propose?✤ Address multimobidity at CG level:
✤ scalable in number of guidelines✤ reusable rules designed for diverse types of interactions✤ binary cumulative rules - allows for combination of n
recommendations
✤ represent recommendations based on transitions promoted by actions✤ do give aspirin x don’t give aspirin
=> different recommendations about the same action✤ hierarchy of actions✤ causation beliefs
✤ reuse of existent background knowledge available online (LOD)
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Case Study
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Case Study
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Case Study
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Case Study
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Case Study
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Case Study
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Case Study
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Case Study
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Reusable Rules IF Positive recommendation R1 to action A1 & Negative recommendation R2 to action A2 & Actions A1 and A2 are the same or subsuming one
anotherTHEN R1 and R2 might contradict each other
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FOL Rules
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Systematic Analysis
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Systematic Analysis
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Case Study
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Conclusion so far✤ Address multimobidity at CG level:
✤ reusable/domain-independent rules for detecting types of interactions
✤ scalable in number of guidelines
Next step✤ Re-use of existent background knowledge available
online (LOD)
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Ibuprofen
Incompatible Drugs
Drugbank
Aspirin
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Ibuprofen
Incompatible Drugs
Drugbank
Aspirin
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Ibuprofen
Incompatible Drugs
Drugbank
Aspirin
Incompatible
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Ibuprofen
Drugbank
Aspirin
Anti-platelets
Epoprostenol
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Ibuprofen
Drugbank
Aspirin
Anti-platelets
Epoprostenol
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Ibuprofen
Drugbank
Aspirin
Anti-platelets
Epoprostenol
Alternative Drug
Alternative Drug
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Ibuprofen High Blood Pressure
Side-EffectSide-Effect
Sider
ThiazideHigh BloodSugar Level
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Ibuprofen High Blood Pressure
Side-EffectSide-Effect
Sider
ThiazideHigh BloodSugar Level
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Ibuprofen High Blood Pressure
Side-EffectSide-Effect
Sider
ThiazideHigh BloodSugar Level
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Ibuprofen High Blood Pressure
Side-EffectSide-Effect
Sider
ThiazideHigh BloodSugar Level
Side-Effect
Side-Effect
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Using LOD
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Using LOD
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Using LOD
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Using LOD
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Using LOD
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Using LOD
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Using LOD
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Using LOD
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Our guideline model in LOD using NanoPublications standard
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NanopublicationProvenance about the publication:When, by whom, how this publication was produced…
“Atomic” piece of information:E.g. causation beliefs, recommendations.
Provenance about the assertion:Who/where it was originally asserted;In case the source is a text, the specific piece od text can also be pointed out.
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Nanopublication
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Nanopublication
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Conclusion ✤ Guideline model: represent recommendations
based on transitions promoted by actions(hierarchy of actions & causation beliefs)
✤ Address multimobidity at guideline level✤ scalable in number of guidelines✤ reusable rules designed for diverse types of interactions
✤Semantic web technology✤ reuse of existent background knowledge available online (LOD)✤ use nano publications for guideline model
Smart Ward: use of guidelines
Smart Ward: interactions detection independent
of specific diseases
Relevant technology
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