Lifestyle is a major factor in a person's health. By making lifestyle computable, it may be possible to guide health.

1. 12/12/2016 ISM 2016,

2. Medicine or Health!
Today: Medicine
• Reactive
• Broad demographic models
• Medicine based cure
• Real time only in ICU
• Anecdotal patient data
• Doctor knows the best
Tomorrow: Health: Health
• Predictive and Preventive
• personalized and Precise
• Lifestyle + medicine
• Real time: Womb to Tomb
• 24/7 data collection
• Patient knows the best

3. Most Fundamental Problem:
Connecting People’s Needs to
Resources Effectively, Efficiently,
and Promptly in given Situations.
3

4. Hunter Gatherer:
When needed, you went
to Food.
Now food comes to you.

5. Until 2000, you went
to a source -- Library.
Now Information finds you.

6. Disruption in Healthcare
When needed, I go to
the source of
healthcare.
Can healthcare come
to me in time?

7. Important Revolution in Health
In the mid-20th century, the primary causes of death
worldwide shifted from infections to chronic conditions.

8. Personal Health
What’s Lifestyle got to do with it?

9. Needed: Medical Emancipation
Doctor Knows the Best.
Patient Knows the Best.

10. Smartphone + Wearables :=
Personalized 24/7 Recording Stethoscope

11. Event Mining
Machine Learning
Individual Model
used in Cybernetic
Health
‘Likely to get severe heart
attack in 10 minutes – get him
help immediately.’
• Individual Model from data.
• Health, social, personal.
• Actionable Predictive use.
• Better disease models.
Healthcare 2020
Objective Self

13. Quantifying Lifestyle
Physical
Activities
Popular now: Output and state
Food
Starting to happen:
Input
Environmental
Factors
Coming soon:
Surround

14. “Shallow men believe in luck or in circumstance. Strong men
believe in cause and effect.”
― Ralph Waldo Emerson

15. What is Cyber Space?
Who invented it?
Animals
Machines
Societies
Published first in 1942

16. Cybernetics: Real Time Feedback Control
Physical
World
And
Information
Systems
Environment and Resources
Information
Personal Situation and Needs
Information
Matching
Action Signals

17. Cybernetics: Personal

18. Future Health: Personal, Predictive, Precise,
Persuasive, and Preventive

19. P5 in Action

20. Quantifying Lifestyle
Measure
Understand
Control
Improve
If you can't measure it, you
can't control it!

21. OBJECTIVE SELF (FUTURE WORK)
OBJECTIVE SELF
Anecdotal
Diarizing data
Quantified Self

22. From data streams to situations
Situations/
Conditions
Event Streams
Observation Streams
Low-level
Analysis
Aggregation
and
Classification
22
Understanding
Verification

23. Data Streams
From Data Streams to Abstracted Event
Streams
Data Management
Event Streams
23
Data Streams

25. Event Stream

26. DailyLife
Activity
ATUS: American Time Use Survey

27. Daily Life Activities: How we enter them?
Chronicle of Daily
Life Activities

28. We collect diverse signals.

29. Personal Event Streams in a Personicle
Daily Activity
Environment
Medical
Emotion
Activity
Food Food Score
Activity Score
Emotion Score
Medical Score
Environment
Score
Health Score = F (Food, Activity, Emotion, Medical, Environment)

30. Running Example
30
Daily Events
WalkingMeeting Break
Arrive
Home
Exercise Asthma Attack
Pollution Events
91 4 6 10 15 19
Low
Temperature Events
Increase Suddenly High
Decrease SteadilyMedium Low
7 14
Leave
Home
Heart rate Events
NormalHigh Elevated

31. Definitions
• Time Interval: [∂, ts, te] ∂+ = ts , ∂− = te
• Semi Interval: [∂+/−, t]
• Point Event (pE): e = (ν, [ E, t])
• Interval Event (iE): e = (v, [E, ts, te])
• Semi-interval Event (sE): e = (v, [E+/−, t])
31
Life Events
WalkingMeeting Break
Arrive
Home
Exercise Asthma Attack
91 4 6 10 15 197 14
Leave
Home

32. Definitions (Cont.)
• Event Stream: ES(i) = {e1
(i), e2
(i), ..., en
(i)}
• Multi-Event Stream: ES = {ES(1), ES(2), ..., ES(∣I∣)}
• Pattern: ρ = (X1 ⊙1 X2 ⊙2 ... ⊙k−1 Xk )
Xi ∈ { pE, iE, sE } and ⊙i ∈ { ; , ;ω∆t , , ∣ }
32
Life Events
WalkingMeeting Break
Arrive
Home
Exercise AsthmaAttack
91 4 6 10 15 197 14
Leave
Home
( Meeting ; Break)
( Exercise+ ;ω[5] AsthmaAttack)
( Walking ; ArriveHome)

33. Representations
33
Daily Events
WalkingMeeting Break
Arrive
Home
Exercise Asthma Attack
Pollution Events
91 4 6 10 15 19
1
Low
8 1
0
24
Temperature Events
Increase Suddenly High
1
Decrease SteadilyMedium Low
247 1
4
7 14
Leave
Home
(( Exercise+ Pollution.High) ;ω[5] AsthmaAttack)
(( Exercise+ Temp.Low ) ;ω[5] AsthmaAttack)
(( Exercise+ (Pollution.High | Temp.Low )) ;ω[5] AsthmaAttack)

34. Pattern Mining Operators
Sequential Co-occurrence
Concurrent Co-occurrence
34

35. 0.25 0.31 0.47 0.11 0.68 0
0.01 0.33 0.64 0.19 0 0.16
0.52 0.12 0.09 0 0.11
0.67 0.1 0 0.7 0.03 0.52
0.13 0 0.75 0.71 0.23 0.25
0 0.43 0.66 0.1 0.2 0.35
Δt
E1 E2 E3 E4 E5 E6
E1
E2
E3
E4
E5
E6
0.88
Co-occurrence Matrix Visualization
0.25
0.31 0.52 0.11 0.23 00.33
0.23 0.4 0.28 0 0.23
0.1 0.45 0 0.11
0.56 0.12 0 0.45 0.4 0.52
0.23 0 0.12 0.23 0.31
0 0.23 0.56 0.1 0.33 0.25
0.280.82
0.82
E1 E2 E3 E4 E5 E6
E1
E2
E3
E4
E5
E6
35

36. Cause - Effect Pattern Structure
event1 event2
event3
event4
Effect
Time lag between
events
Sequence of events
Events in parallel
Cause 1
Cause 2
Cause 3
(No medication ; Exercise)  Asthma attack
(Exercise Pollen high)  Asthma attack
(Exercise (Pollen high | pollution high))  Asthma attack
(Exercise Pollution high)  Asthma attack
Exercise  Asthma attack
Δt
Formulate and query complex patterns:

37. Pattern
Mining
High level
Pattern
Formulation
Pattern
Query
…
Data Streams Event Streams Semi-interval Event Sequences
Data-Driven Analysis
Hypothesis-Driven Analysis
While air pressure is high,
pollution starts increasing
gradually, within T time units
asthma outbreak happens.
((pollution_inc_steadily ;ωT asthma_outbreak ) ||
airpressure_stayshigh)
Example:
Interactive Visualization
Interactive Event Mining

38. User Interface for Interactive Knowledge
Discovery and Model Building
Data-Driven
Hypothesis-
Driven

39. Pollution and Meteorological Data

40. Asthma Risk Factor Recognition
1) Pollution increases
suddenly followed by high
wind while temperature
increases slightly will cause
an asthma
Outbreak within 2 days.
2) Thunderstorm followed
by temperature decreases
steadily will cause an
asthma outbreak within 1
day.

41. Results
Temperature fluctuation has the most impact in the fall and winter seasons
and it is not a risk factor during spring or summer
During spring and summer, when rain suddenly increases to a very high level,
an asthma outbreak is more probable
The effect of PM2.5 is not noteworthy in
the fall and winter seasons.

42. Results (Cont.)
• When PM2.5 increases followed by temperature stay high within 3 days,
then asthma outbreak is probable.
• When wind decreases followed by PM2.5 increases within 5 days, then
asthma outbreak is probable.
• When rain increases followed by PM2.5 stay low within 4 days then an
asthma outbreak is probable.

43. Spicy Indian food and 2 glasses of wine
result in severe acidity and sleepless nights.
Personicle
Food Stream
t1 t2 t3 t4 t5
Warn him when he is at an Indian
Restaurant.

44. Future Health:
• Personalized
• Predictive
• Precise
• Persuasive
• Preventive
Right Moment,
Right Place,
Right Decision,
Right way.

45. Health Butler
Your food and
activity scores for
the last 3 days are
on low side.
Coming Soon Near You

46. Current Status
• Institute for Future Health
• Event Framework getting ready
• First Application is Diabetes
– Next Cardiovascular and Asthma
• Several partners: UCSD, Western Sydney, City
University Hong Kong, University of Tokyo,
EPFL, …
• Expect to release first version of Health Butler
in 2nd quarter, 2017.