Presentation at 2nd transatlantic conference on personalized medicine TCPM2015 Rotterdam.

1. THE SHARED VALUE OF PERSONAL
AND POPULATION DATA
Trusted architectures, self management, patient driven hypotheses | Wessel Kraaij
TNO technical sciences, The Hague
Radboud University, Nijmegen
Wessel.kraaij@tno.nl

2. HEALTH IS MORE THAN MOLECULES
Health is complex
Body and mind are integrated
Environment is important
(physical and social)
 Multiple relations and data !
TNO Early Research Programmes
(2015-2018)
• Making Sense of Big Data
• Grip on Health (Complexity)
• Personalized Food
• Human Resilience

3. QUANTIFIED SELF DATA FOR HEALTH?
3
bron: MIT
QS: A movement of citizens and
‘makers’ that aim to explore the
possibilities of self-tracking.
Gary Wolf (Wired): “Almost
everything we do generates
data”.
WHAT COULD WE LEARN IF?
we could measure and record
activities, social context,
environmental context, physiological
parameters, food intake, sleep across
our entire lifetime? Now we have
sensors for most of these..
75% NL
population
owns
smartphone
• Always on…
• Online
• Multiple sensors
• Located near people

4. IMPLICATIONS
New data acquisition methods
ICT challenges
New research questions and challenges
Data science, causal inference
New dynamic (ownership, control points, agenda setting)
Crucial:
Trust (ethical, legal policies)
Data governance
ICT , privacy by design
4

5. HEALTH CARE DATA:
DIFFERENT STAKEHOLDERS, DIFFERENT
INTERESTS
Insurance: minimize cost of care
Hospitals: Optimize processes (# successful treatments)
Researchers: collect data for studies (# top publications)
Tech platform companies (Google/Apple): pervasive monitoring of
personal data (#users)
Patient interest?

6. Future scenario #1:
Uber health
Uber connects patients and
caregivers (private clinics) directly
Uber owns patient health data
FUTURE SCENARIOS
6
Future scenario #2:
Google health
Google recommends you to visit a GP nearby
with high evaluations as soon as it finds that your
pulse shows irregularities
Google owns patient health data
Future scenario #3: Value based HC
(Porter)
Core value:
‘health outcomes that matter to patients’
Patients own their own data.

7. A brief overview of the COMMIT/ SWELL project
The ‘personalized health’
perspective
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8. SWELL Project
• Goal
• Keep knowledge workers healthy
• Mental: workload, stress, burn-out
• Physical: activity levels, sleep
• Approach
A. Estimating mental and physical state by combining
multiple unobtrusive sensor streams
B. Offer support based on BCT
Smart reasoning systems for WELL-being (7 Meuro, 5 yr)

9. INFORMATION FROM SENSORS
Work characteristics: work tasks, topics
Stress related variables: task load, mental effort, emotion, perceived stress

10. SWELL WORKLOAD MIRROR
How to visualize sensor data?

11. SWELL PROTOTYPES
Brightr e-coach
SWELL
Fishualization:
SWELL NiceWork e-
coach:
Deal with
jetlag
real time mapping of sensor data
recommender system for tips

12. WHAT DID WE LEARN: PRIVACY
CONCERNS
Candidate subjects did have concerns to share data
Main concern was sharing detailed data with manager
Degree of privacy concerns varies significantly across subject
Project focus choice:
Subjects (employees) control access to personal data
No aggregation
[Koldijk, S., Koot, G., Neerincx, M.A., & Kraaij, W. (2014). Privacy and User Trust in Context-Aware
Systems. In: Proceedings of the 22nd Conference on User Modeling, Adaptation and Personalization
(UMAP 2014) (Aalborg, Denmark, 7-11 July 2014).]
Project design choice:
=>Subjects (employees) control access to personal data
=>No aggregation, pure self management

13. The need for reference data and/or
models in Pmed & Phealth
13

14. REFERENCE DATA NEEDED FOR CLINICAL
REASONING AND SELF MANAGEMENT
Example: Fitness metric VO2 Max depends on gender, age, genetics and
body size
Population peer data helps to interpret fitness level
Example: Growth data of large cohort of infants can be used to estimate the
effect of an intervention for an individual child
[van Buuren S (2014) Curve Matching: A Data-Driven Technique to
Improve Individual Prediction of Childhood Growth. Annals of Nutrition
& Metabolism, 65(3), 225-231]
Helps to reduce under/overtreatment based on average causal
effect reasoning
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Data driven
approach

15. Data Model
User Interface
Advice
Lifestyle
advice
BUILDING PERSONALIZED SYSTEM-BASED
HEALTH ADVICE SYSTEMS
Measurements
Interventions
Behaviour
change
Health
Projections
Data cooperative
Food intake
Wellbeing
Modelling health
Personalized
food advice
N=1 analysis
Health biomarkers
Health visualization
Phenflex II
Hybrid approach:
data & model

16. THE MUTUAL DEPENDENCE OF PERSONAL AND
POPULATION HEALTH DATA
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Bron: cbw.ge en wikimedia.org
Van ‘big’ naar ik

17. The health data infrastructure
challenge
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18. TOWARDS PERSONAL HEALTH DATA
STORES (1)
Starting point: a single
infrastructure for:
Patients
Control and ownership of data
Fine grained access policies
Coverage: from conception to
end of life
Research
FAIR principles (Findable,
Accessible, Interoperable,
Reusable), anonimized
aggregated
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Health professionals
Data from similar individuals
supports clinical reasoning
(Pseudonymized)
Government & Industry
Access anonimized
aggregated data

19. TOWARDS PERSONAL HEALTH DATA
STORES (2)
Support different vendors of DIY measurements
Some measurements will be computed locally and aggregated to acceptable
privacy disclosure level
Ecosystem of different PHDS providers e.g. Apple, Google, Health Data
Cooperative, regional health data intermediary, ISP/cloud solutions
Should support minimal standards
Should support efficient patient similarity search
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20. Example of a patient group driven study:
exploring what matters to GIST patients
GIST: rare cancer 15 over 1,000,000 new cases annually
GIST Facebook group study
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21. GIST FACEBOOK MINER
Aggregating patient data is particularly relevant for rare diseases
Search and explore web forum discussions for patients and hypothesis
generation
Combining deep learning (Word2vec) and domain knowledge (Concept
tagging)
Word2vec
Generates a semantic model of a large text corpus based on word contexts.
Example: Gleevec: Glivec, Sutent, Mg, 400 Mg, Gleevic
Concept tagging of UMLS (Unified Medical Language System) concepts
I’m on a clinical trial with the drug Ponatinib on a daily dosage of 30 mg trying to
shrink 8 tumors.
Research Activity Medicines Temporal Concept Neoplastic ProcessGIST

22. CONCEPTS RELATED
TO “ITCHY”
GIST

23. GIST

24. CONCLUSIONS
1. New forms of personal data have a potential for improving individual
health outcomes and prevention
Combination of different data sources, new sensors, patient networks, models
2. The potential is best realized when compared with population or peer
data
We also need data about healthy people
But many of the new measures are privacy sensitive
This is a serious barrier for uptake on a large scale
3. A health data infrastructure should be built on ‘privacy by design’
principles to safeguard the interests of all stakeholders
Interoperability between different flavours: Google/Apple; health data
cooperatives, personal data stores
Access controlled by owners
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25. CALL TO ACTION
Dutch ICT sector promotes a large R&D
programme on Big Data:
COMMIT2DATA
Life (including Health) is one of the
focal domains
personal and population data
We are looking for partners to co-create
a health data infrastructure built on the
principles of shared value
5-Oct-2015

26. THANK YOU FOR YOUR ATTENTION
Wessel.Kraaij@tno.nl