RuleML 2015: Ontology Reasoning using Rules in an eHealth Context

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

Ontology Reasoning using Rules in an eHealth Context
Outline
Business Case
Technological Challenges
Rule Based Solution
Results
Importance and Impact

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

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

Why rule based solution?
Classical
(Java, C++, …)
OWL DL +
SPARQL
Rule based
Scalability   
Semantics   
Functional
Complexity
  
Configuration   
Real Time   

Available data

Available data
Our ontology (ACCIO ontology) is filled with:

Available data
• Location of personnel and patients

Available data
• Current task of care givers (what is he/she doing?)

Available data
• Competences of staff members

Available data
• Special needs of patients

Available data
• Relationship of nurses and patients

Available data
• (Possible) Reasons for calls

Available data
• (Possible) Reasons for calls
And much more…

• For reasoning we used the EYE reasoner
• We used Notation3 Logic to express
• OWL RL rules
• Decision Trees
Rules

OWL RL in N3
As an example we take subClassOf:
{?C rdfs:subClassOf ?D. ?x a ?C.} => {?x a ?D.}.

OWL RL in N3
Knowledge:
:Call rdfs:subclassOf :Task. :call1 a :Call.

OWL RL in N3
Knowledge:
We get:

OWL RL in N3
Knowledge:
We get:
:call1 a :Task.

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 : madeAtLocation ?loc.
?p : hasStatus : Free .
?p : closeTo ?loc.
}
=>
{
}.
Priority
0
200
100

Decision Tree
{
rdf: type :
].
}
=>
{
}.
{
?c : madeAtLocation ?loc.
?p : hasStatus : Free .
?p : closeTo ?loc.
}
=>
{
}.
Priority
0
100
200
Priorities can be changed
easily

Decision Tree
{
rdf: type :
].
?c :madeAtLocation ?loc.
?p :closeTo ?loc.
}
=>
{
}.
New triples can be
added easily

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)

Results: 1 ward

Results: 10 wards

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

Rule based reasoning can be used in future products of televic healthcare

RuleML 2015: Ontology Reasoning using Rules in an eHealth Context

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RuleML 2015: Ontology Reasoning using Rules in an eHealth Context