linked open data for medical guidelines interactions

@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

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

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

What about…✤ Computer-interpretable

Clinical Guideline (CIG)✤ How to combine CIGs?

✤ CIG Languages: ✤ mainly designed for execution✤ not suitable for detecting

interactions

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)

Case Study

Case Study

Case Study

Case Study

Case Study

Case Study

Case Study

Case Study

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

FOL Rules

Systematic Analysis

Systematic Analysis

Case Study

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)

Ibuprofen

Incompatible Drugs

Drugbank

Aspirin

Ibuprofen

Incompatible Drugs

Drugbank

Aspirin

Ibuprofen

Incompatible Drugs

Drugbank

Aspirin

Incompatible

Ibuprofen

Drugbank

Aspirin

Anti-platelets

Epoprostenol

Ibuprofen

Drugbank

Aspirin

Anti-platelets

Epoprostenol

Ibuprofen

Drugbank

Aspirin

Anti-platelets

Epoprostenol

Alternative Drug

Alternative Drug

Ibuprofen High Blood Pressure

Side-EffectSide-Effect

Sider

ThiazideHigh BloodSugar Level

Ibuprofen High Blood Pressure

Side-EffectSide-Effect

Sider

ThiazideHigh BloodSugar Level

Ibuprofen High Blood Pressure

Side-EffectSide-Effect

Sider

ThiazideHigh BloodSugar Level

Ibuprofen High Blood Pressure

Side-EffectSide-Effect

Sider

ThiazideHigh BloodSugar Level

Side-Effect

Side-Effect

Using LOD

Using LOD

Using LOD

Using LOD

Using LOD

Using LOD

Using LOD

Using LOD

Our guideline model in LOD using NanoPublications standard

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.

Nanopublication

Nanopublication

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