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.

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)

6. Case Study

7. Case Study

8. Case Study

9. Case Study

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

25. 32
Ibuprofen
Drugbank
Aspirin
Anti-platelets
Epoprostenol
Alternative Drug
Alternative Drug

26. 33Ibuprofen
High Blood
Pressure
Side-EffectSide-Effect
Sider
Thiazide
High Blood
Sugar Level

27. 34Ibuprofen
High Blood
Pressure
Side-EffectSide-Effect
Sider
Thiazide
High Blood
Sugar Level

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