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RuleML 2015: Ontology Reasoning using Rules in an eHealth Context

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Traditionally, nurse call systems in hospitals are rather simple:
patients have a button next to their bed to call a nurse. Which specific
nurse is called cannot be controlled, as there is no extra information
available. This is different for solutions based on semantic knowledge:
if the state of care givers (busy or free), their current position, and for
example their skills are known, a system can always choose the best
suitable nurse for a call. In this paper we describe such a semantic nurse
call system implemented using the EYE reasoner and Notation3 rules.
The system is able to perform OWL-RL reasoning. Additionally, we use
rules to implement complex decision trees. We compare our solution to
an implementation using OWL-DL, the Pellet reasoner, and SPARQL
queries. We show that our purely rule-based approach gives promising
results. Further improvements will lead to a mature product which will
significantly change the organization of modern hospitals.

Published in: Science
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RuleML 2015: Ontology Reasoning using Rules in an eHealth Context

  1. 1. ELIS – Multimedia Lab Dörthe Arndt, Ben De Meester, Pieter Bonte, Jeroen Schaballie, Jabran Bhatti, Wim Dereuddre, Ruben Verborgh, Femke Ongenae, Filip De Turck, Rik Van de Walle, and Erik Mannens Multimedia Lab, Ghent University - iMinds, Belgium Internet Based Communication Networks and Services , Ghent University - iMinds, Belgium Televic Healthcare - Izegem, Belgium RuleML 2015, Berlin, August 05, 2015 Ontology Reasoning using Rules in an eHealth Context
  2. 2. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Outline Business Case Technological Challenges Rule Based Solution Results Importance and Impact
  3. 3. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Outline Business Case Technological Challenges Rule Based Solution Results Importance and Impact
  4. 4. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Adaptable context aware nurse call system for Why do they want that? • Less distraction for nurses, if they are busy they don’t get called • Less walking distances for nurses and doctors • No time loss by assigning calls to persons without the necessary competences • Trust between personell and patients • Adaptivity to the requirements of any hospital →More efficient organization of hospitals Business Case
  5. 5. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Outline Business Case Technological Challenges Rule Based Solution Results Importance and Impact
  6. 6. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Technological Challenges • Scalability cope with data sets ranging from 1000 to 100 000 relevant triples • Semantics be able to draw conclusions based on the information it is aware of • Functional complexity implement deterministic decision trees with varying complexities • Configuration have the ability to change these decision trees at configuration time • Real-time return a response within 5 seconds to any given event
  7. 7. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Why rule based solution? Classical (Java, C++, …) OWL DL + SPARQL Rule based Scalability    Semantics    Functional Complexity    Configuration    Real Time   
  8. 8. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Outline Business Case Technological Challenges Rule Based Solution Results Importance and Impact
  9. 9. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Available data
  10. 10. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Available data Our ontology (ACCIO ontology) is filled with:
  11. 11. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Available data Our ontology (ACCIO ontology) is filled with: • Location of personnel and patients
  12. 12. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Available data Our ontology (ACCIO ontology) is filled with: • Location of personnel and patients • Current task of care givers (what is he/she doing?)
  13. 13. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Available data Our ontology (ACCIO ontology) is filled with: • Location of personnel and patients • Current task of care givers (what is he/she doing?) • Competences of staff members
  14. 14. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Available data Our ontology (ACCIO ontology) is filled with: • Location of personnel and patients • Current task of care givers (what is he/she doing?) • Competences of staff members • Special needs of patients
  15. 15. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Available data Our ontology (ACCIO ontology) is filled with: • Location of personnel and patients • Current task of care givers (what is he/she doing?) • Competences of staff members • Special needs of patients • Relationship of nurses and patients
  16. 16. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Available data Our ontology (ACCIO ontology) is filled with: • Location of personnel and patients • Current task of care givers (what is he/she doing?) • Competences of staff members • Special needs of patients • Relationship of nurses and patients • (Possible) Reasons for calls
  17. 17. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Available data Our ontology (ACCIO ontology) is filled with: • Location of personnel and patients • Current task of care givers (what is he/she doing?) • Competences of staff members • Special needs of patients • Relationship of nurses and patients • (Possible) Reasons for calls And much more…
  18. 18. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context • For reasoning we used the EYE reasoner • We used Notation3 Logic to express • OWL RL rules • Decision Trees Rules
  19. 19. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context OWL RL in N3 As an example we take subClassOf: {?C rdfs:subClassOf ?D. ?x a ?C.} => {?x a ?D.}.
  20. 20. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context OWL RL in N3 Knowledge: :Call rdfs:subclassOf :Task. :call1 a :Call. As an example we take subClassOf: {?C rdfs:subClassOf ?D. ?x a ?C.} => {?x a ?D.}.
  21. 21. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context OWL RL in N3 Knowledge: :Call rdfs:subclassOf :Task. :call1 a :Call. As an example we take subClassOf: {?C rdfs:subClassOf ?D. ?x a ?C.} => {?x a ?D.}.
  22. 22. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context OWL RL in N3 Knowledge: :Call rdfs:subclassOf :Task. :call1 a :Call. As an example we take subClassOf: {?C rdfs:subClassOf ?D. ?x a ?C.} => {?x a ?D.}.
  23. 23. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context OWL RL in N3 Knowledge: :Call rdfs:subclassOf :Task. :call1 a :Call. As an example we take subClassOf: {?C rdfs:subClassOf ?D. ?x a ?C.} => {?x a ?D.}.
  24. 24. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context OWL RL in N3 Knowledge: :Call rdfs:subclassOf :Task. :call1 a :Call. As an example we take subClassOf: We get: {?C rdfs:subClassOf ?D. ?x a ?C.} => {?x a ?D.}.
  25. 25. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context OWL RL in N3 Knowledge: :Call rdfs:subclassOf :Task. :call1 a :Call. As an example we take subClassOf: We get: {?C rdfs:subClassOf ?D. ?x a ?C.} => {?x a ?D.}.
  26. 26. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context OWL RL in N3 Knowledge: :Call rdfs:subclassOf :Task. :call1 a :Call. As an example we take subClassOf: We get: :call1 a :Task. {?C rdfs:subClassOf ?D. ?x a ?C.} => {?x a ?D.}.
  27. 27. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Decision Tree { ?c rdf : type : Call . ?c : hasStatus : Active . ?c : hasReason [rdf: type : CareReason ]. ?p rdf : type : Person . ?p : hasStatus : Busy . ?p : hasRole [rdf: type : StaffMember ]. ?p : hasCompetence [ rdf: type : AnswerCareCallCompetence ]. } => { (?p ?c) : assigned 100. }. { ?c rdf : type : Call . ?c : hasStatus : Active . ?c : madeAtLocation ?loc. ?p : hasRole [rdf: type : StaffMember ]. ?p : hasStatus : Free . ?p : closeTo ?loc. } => { (?p ?c) : assigned 200. }. Priority 0 200 100
  28. 28. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Decision Tree { ?c rdf : type : Call . ?c : hasStatus : Active . ?c : hasReason [rdf: type : CareReason ]. ?p rdf : type : Person . ?p : hasStatus : Busy . ?p : hasRole [rdf: type : StaffMember ]. ?p : hasCompetence [ rdf: type : AnswerCareCallCompetence ]. } => { (?p ?c) : assigned 200. }. { ?c rdf : type : Call . ?c : hasStatus : Active . ?c : madeAtLocation ?loc. ?p : hasRole [rdf: type : StaffMember ]. ?p : hasStatus : Free . ?p : closeTo ?loc. } => { (?p ?c) : assigned 100. }. Priority 0 100 200 Priorities can be changed easily
  29. 29. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Decision Tree { ?c rdf : type : Call . ?c : hasStatus : Active . ?c : hasReason [rdf: type : CareReason ]. ?p rdf : type : Person . ?p : hasStatus : Busy . ?p : hasRole [rdf: type : StaffMember ]. ?p : hasCompetence [ rdf: type : AnswerCareCallCompetence ]. ?c :madeAtLocation ?loc. ?p :closeTo ?loc. } => { (?p ?c) : assigned 200. }. New triples can be added easily
  30. 30. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Outline Business Case Technological Challenges Rule Based Solution Results Importance and Impact
  31. 31. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Testscenario 1. A patient launches a call (assign nurse and update call status) 2. The assigned nurse indicates that she is busy (assign other nurse) 3. The newly assigned nurse accepts the call task (update call status) 4. The nurse moves to the corridor (update location) 5. The nurse arrives at the patients’ room (update location, turn on lights and update nurse status) 6. The nurse logs in to the room’s terminal (update status call and nurse, open lockers) 7. The nurse logs out again (update status call and nurse, close lockers) 8. The nurse leaves the room (update location and call status and turn off lights)
  32. 32. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Results: 1 ward
  33. 33. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Results: 10 wards
  34. 34. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context Outline Business Case Technological Challenges Rule Based Solution Results Importance and Impact
  35. 35. ELIS – Multimedia Lab Ontology Reasoning using Rules in an eHealth Context We learned: • Applying rule based reasoning instead of OWL DL & SPARQL makes a difference • First results are promising • For small data sets we meet the requirements • Reasoning times are stable • Decision trees are easy to handle via rules → Further improvements will lead to faster implementations Importance and Impact
  36. 36. Rule based reasoning can be used in future products of televic healthcare

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