This document discusses managing patient health trajectories through decentralized semantic web agents. It addresses the challenges of modeling dynamic patient trajectories from heterogeneous data sources while ensuring data privacy and integration. An ontology is proposed to semantically represent medical concepts within patient trajectories. Agents would cooperate to acquire, aggregate, analyze and predict trajectory data in a decentralized manner. Future work involves further developing the trajectory modeling and validating the agent-based approach through implementation.

1. Decentralized Management of Patient Profiles and
Trajectories through Semantic Web Agents
Jean-Paul Calbimonte • Davide Calvaresi • Michael Schumacher
University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
3rd International Workshop on Semantic Web Meets Health Data Management
International Semantic Web Conference ISWC 2020
November 2020
@jpcik

2. 2
Context: long-term patient support
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sustained support
• chronic diseases
• post-surgery
• cancer survivors
diagnosis
treatment
surgery
eHealth patient
support
Distributed care +
heterogeneous clinical
history

3. 3
Patient Trajectorieswellbeingdistress
diagnosis
therapy surgery
discharge
physical
psychological
social
timeline
Illness
trajectory
Patient
trajectory patient context
emotional/ social indicators
self-reported outcomes
monitoring observations
diagnosis
treatment
events

4. 4
Case scenario: Cancer survivors support
Patients-centered Survivorship care plan after Cancer
treatments based on Big Data and Artificial Intelligence
technologies
diagnosis
chemo
surgery
physical rehab
survivor trajectory
AIsupporttech
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5. 5
Issues in trajectory management
Trajectory information
heterogeneity
Patient data sources
diversity
Trajectory data
integration & aggregation
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demographics diagnosis morbidities treatments
. . .
sensors AI self-reported EHR
. . .

6. 6
Issues in trajectory management
Life-long dynamic
trajectories
Data analytics
explainability
Privacy and
confidentiality
trend
analysis
pattern
discovery
trajectory
classification
explainable outcomes
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cohort
trajectories
dataprivacy+trust+
compliance

7. 7
Challenges
1
Trajectory modelling
Decentralization
Cooperation
2
3
Data volume & velocity
Data privacy
5
4

8. 8
Ontology-based trajectory modelling
schema:Patientgender
schema:MedicalSymptom
schema:MedicalCause
schema:MedicalTherapy
schema:MedicalIndication
schema:DoseSchedule
schema:Drug
schema:MedicalEntity
schema:MedicalCause
schema:DDxElement
schema:Drug
schema:MedicalRiskFactor
schema:MedicalConditionStage
schema:MedicalTherapy
schema:AnatomicStructure
schema:MedicalCondition
schema:Drug
schema:MedicalCondition
Calbimonte, J.P., Dubosson, F., Schumacher, M.: Towards semantic models for proceeding and behavior change in ehealth
applications. In: Proc. of the PD Track of the 15th Intl. Conference on Semantic Systems SEMANTiCS. pp. 41–45 (2019)

9. 9
Ontology-based modellingwellbeingdistress
diagnosis
therapy surgery
discharge
physical
psychological
social
timeline
schema:Patient
schema:MedicalSymptom
schema:MedicalCondition
schema:MedicalTherapy
schema:SurgicalProcedure
schema:MedicalCondition

10. 10
Decentralized Data Management
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...
user
profile
state
machine
domain
model
comp.
persuasion
privacy sphere
coordinator agent
mediator agents
coach agent
behavior agent
Behavior change agent
useragents
users
bot interactions inter-agent interactions
Calbimonte, J.P., Calvaresi, D., Dubosson, F., Schumacher, M.: Towards profile and domain modelling in agent-based
applications for behavior change. In: Intl Conf on Practical Applications of Agents and Multi-Agent Sys. pp. 16–28 (2019)

11. 11
Inter-agent cooperation & negotiation
trajectories
beliefs
behaviors
τAgent
τAgent
τAgent
τAgent trajectory data
acquisition
beliefs
metadata
health ontologies
constraints
privacy policies
behaviors
negotiation
cooperation
reasoning
ML gateways
role-specific
goals
scheduler
channels
MLprocessing
τAgent
ontologies
trajectory
negotiation
trajectory data
analysis
trajectory
aggregation
channels
Calvaresi, D., Schumacher, M., Calbimonte, J.P.: Agent-based modeling for ontology-driven analysis of
patient trajectories. J. Med. Systems 44(9), 1–11 (2020)

12. 12
Inter-agent cooperation & negotiation
Patient
τAgent
Coaching
τAgent
Aggregator
τAgent
Processing
τAgentPatient
τAgents
Sensor τAgents
trajectory data
data acquisition
support +
recommendation
pre-processed
trajectories
predictions,
analytics,
classification
trajectory data
Calvaresi, D., Schumacher, M., Calbimonte, J.P.: Agent-based modeling for ontology-driven analysis of
patient trajectories. J. Med. Systems 44(9), 1–11 (2020)

13. 13
Real-time stream processing agents
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RSP Agent
mailbox
beliefs
behavior
RSP Agent
RSP Agent
RSP Agent
RSP Agent
RDF stream
metadata
metadata
RDF stream
beliefs
metadata
stream windows
background RDF
ontology TBox
behavior
message dispatch
RDF stream pull
RDF stream push
RSP querying
reasoning
RT constraints
goals
scheduler

14. 14
Privacy-aware agents for data exchange
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Subject
Processor
Recipient
consent
consent
Controller
analytics request
Calvaresi, D., Calbimonte, J.P.: Real-time compliant stream processing agents for
physical rehabilitation. Sensors20(3), 746 (2020)
Subject

15. 15
Example: Trajectory predictiondistress
diagnosis
therapy surgery
predictive trajectory
physical
psychological
social
timeline
schema:
MedicalSymptom
schema:
MedicalCondition
schema:
MedicalTherapy
schema:
SurgicalProcedure
schema:
MedicalCondition
schema:
Patient
retrospective trajectory
trajectory 2

16. 16
Example: Agent protocols
Coaching
τAgent
Processing
τAgent
request prediction
inform-result
[trajectory prediction data]
Patient
τAgent
consent
call-for-data
propose
accept
inform-result
self-organizing
protocol
data request
data stream
Patient
τAgents
consent
call-for-retrospective-data
propose
accept
Therapist
τAgent
Sensor
τAgents
[trajectory sensory data]
inform-result
[trajectory data]
notifynotify
[coaching advice] [patient progress]

17. 17
Future work
Trajectory modelling
Implementation & validation
Decentralized explainability
Trust & accountability
Patient empowerment

18. Questions?
Jean-Paul Calbimonte
University of Applied Sciences and Arts
Western Switzerland
HES-SO Valais-Wallis
@jpcik