Linked Open Data for Medical Guidelines Interactions

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An increasing number of patients suffer from multiple diseases at the same time. This makes their treatment much more complex, and the standard medical treatment guidelines no longer apply (they are typically written for patients with just a single disease). We present computer-based techniques for analysing medical guidelines to detect how multiple guidelines may interact in unexpected ways, and how Linked Open Data can be used to recognise and avoid such adverse effects.

Science

@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 Ward
Expectation:
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)

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 another
THEN R1 and R2 might contradict each other

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)

27
Ibuprofen
Incompatible Drugs
Drugbank
Aspirin

28
Ibuprofen
Incompatible Drugs
Drugbank
Aspirin

29
Ibuprofen
Incompatible Drugs
Drugbank
Aspirin
Incompatible

30
Ibuprofen
Drugbank
Aspirin
Anti-platelets
Epoprostenol

31
Ibuprofen
Drugbank
Aspirin
Anti-platelets
Epoprostenol

32
Ibuprofen
Drugbank
Aspirin
Anti-platelets
Epoprostenol
Alternative Drug
Alternative Drug

33Ibuprofen
High Blood
Pressure
Side-EffectSide-Effect
Sider
Thiazide
High Blood
Sugar Level

34Ibuprofen
High Blood
Pressure
Side-EffectSide-Effect
Sider
Thiazide
High Blood
Sugar Level

35Ibuprofen
High Blood
Pressure
Side-EffectSide-Effect
Sider
Thiazide
High Blood
Sugar Level

36Ibuprofen
High Blood
Pressure
Side-EffectSide-Effect
Sider
Thiazide
High Blood
Sugar Level
Side-Effect
Side-Effect

Our guideline model in LOD
using NanoPublications
standard
45

Nanopublication
46
Provenance 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.

Conclusion
49
✤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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Linked Open Data for Medical Guidelines Interactions

1. @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

2. 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 Ward Expectation: multi-morbidity holds for psychiatric patients as well

3. 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

4. What about… ✤ Computer-interpretable Clinical Guideline (CIG) ✤ How to combine CIGs? ✤ CIG Languages: ✤ mainly designed for execution ✤ not suitable for detecting interactions

5. 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)

10. Case Study

11. Case Study

12. Case Study

13. Case Study

14. 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 another THEN R1 and R2 might contradict each other

15. FOL Rules

16. Systematic Analysis

17. Systematic Analysis

18. Case Study

19. 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)

20. 27 Ibuprofen Incompatible Drugs Drugbank Aspirin

21. 28 Ibuprofen Incompatible Drugs Drugbank Aspirin

22. 29 Ibuprofen Incompatible Drugs Drugbank Aspirin Incompatible

23. 30 Ibuprofen Drugbank Aspirin Anti-platelets Epoprostenol

24. 31 Ibuprofen Drugbank Aspirin Anti-platelets Epoprostenol

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28. 35Ibuprofen High Blood Pressure Side-EffectSide-Effect Sider Thiazide High Blood Sugar Level

29. 36Ibuprofen High Blood Pressure Side-EffectSide-Effect Sider Thiazide High Blood Sugar Level Side-Effect Side-Effect

30. Using LOD 37

31. Using LOD 38

32. Using LOD 39

33. Using LOD 40

34. Using LOD 41

35. Using LOD 42

36. Using LOD 43

37. Using LOD 44

38. Our guideline model in LOD using NanoPublications standard 45

39. Nanopublication 46 Provenance 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.

40. Nanopublication 47

41. Nanopublication 48

42. Conclusion 49 ✤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

Editor's Notes

Should administer NSAID to reduce blood coagulation
guideline Diabetes (DB) should administer Tramadol to reduce blood coagulation should administer Insulin to reduce blood sugar level
guideline Osteoarthritis (OA) should NOT administer Aspirin to avoid increasing risk of gastro intestinal bleeding Notice: DB.1 and DB.2 are alternative recommendations meant for promoting the same effect. Interactions: DB1 en DB2 alternatives
Osteoarthritis (OA): should administer Ibuprofen to reduce pain
DB1 and OA1 are contradictory recommendations DB1 might lead to the prescription of Aspirin Aspirin is not recommended by OA1.
third guideline: Hypertension Should administer Thiziade to reduce the blood pressure OA3 and HT1 interact:OA3 has interaction via high blood sugar levels.
dotted lines: beliefs
Alternative drug is Ibuprofen.
Norm —> until now only mentioned recommendation

Linked Open Data for Medical Guidelines Interactions

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Editor's Notes