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10/15/2016 1
Society exists only as a mental concept; in
the real world there are only individuals.
-- Oscar Wilde
Wearables: Watches, camera, …
I don’t care for money;
I care for what money buys.
I don’t care for wearables;
I care for w...
Disruption in Healthcare
When needed, I go to
the source of
healthcare.
Can healthcare come
to me in time?
Event Mining
Machine Learning
Individual Model
‘Likely to get severe heart
attack in 10 minutes – get him
help immediately...
Important Revolution in Health
In the mid-20th century, the primary causes of death
worldwide shifted from infections to c...
Dream: Immortality
Problem: Longest people can live is
122 Yrs.
Realizable Dream:
100+ Years of Happiness
Personal Health
What’s Lifestyle got to do with it?
High Blood Pressure
Heart Disease Allergies
Thyroid Imbalance
Diabetes
Chronic Fatigue
Auto Immune Disease
Cancer
Stress
P...
Needed: Medical Emancipation
Doctor Knows the Best.
Patient Knows the Best.
Smartphone + Wearables :=
Personalized 24/7 Recording Stethoscope
Financial
Physical
Environ-
mental
Emotional
Sexual
Social
Intellectual
Spiritual
Lifestyle
Big 3: Lifestyle Factors
Physical
Activities
Popular now: Output and state
Food
Starting to happen:
Input
Environmental
Fa...
Healthcare Analytics is Data Rich Now
Past: Data is expensive and small
• Input data is mostly from clinical trials
• Mode...
Until recently, you
were a folder.
Now You are Your Data.
Disruption Time
“Shallow men believe in luck or in circumstance. Strong men
believe in cause and effect.”
― Ralph Waldo Emerson
If you can't measure it, you
can't control it!
Measure
Understand
Control
Improve
OBJECTIVE SELF (FUTURE WORK)
OBJECTIVE SELF
Anecdotal
Diarizing data
Quantified Self
Data Streams to Objective Self
Daily Life
Activities
Data Streams
Data Variety: From Data Streams to
Abstracted Event Streams
Data Management
Event Streams
20
Event Stream
DailyLife
Activity
ATUS: American Time Use Survey
Daily Life Activities: How we enter them?
Chronicle of Daily
Life Activities
We collect diverse signals.
Running Example
25
Daily Events
WalkingMeeting Break
Arrive
Home
Exercise Asthma Attack
Pollution Events
91 4 6 10 15 19
L...
Definitions
• Time Interval: [∂, ts, te] ∂+ = ts , ∂− = te
• Semi Interval: [∂+/−, t]
• Point Event (pE): e = (ν, [ E, t])...
Definitions (Cont.)
• Event Stream: ES(i) = {e1
(i), e2
(i), ..., en
(i)}
• Multi-Event Stream: ES = {ES(1), ES(2), ..., E...
Representations
Laleh Jalali
lalehj@ics.uci.edu
Interactive Knowledge Discovery from Data
Streams
28
Daily Events
WalkingM...
Pattern Mining Operators
Sequential Co-occurrence
Concurrent Co-occurrence
29
Laleh Jalali
lalehj@ics.uci.edu
Interactive ...
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...
Cause - Effect Pattern Structure
event1 event2
event3
event4
Effect
Time lag between
events
Sequence of events
Events in p...
Pattern
Mining
High level
Pattern
Formulation
Pattern
Query
…
Data Streams Event Streams Semi-interval Event Sequences
Dat...
User Interface for Interactive Knowledge
Discovery and Model Building
Data-Driven
Hypothesis-
Driven
Pollution and Meteorological Data
Asthma Risk Factor Recognition
1) Pollution increases
suddenly followed by high
wind while temperature
increases slightly ...
Results
Temperature fluctuation has the most impact in the fall and winter seasons
and it is not a risk factor during spri...
Results (Cont.)
• When PM2.5 increases followed by temperature stay high within 3 days,
then asthma outbreak is probable.
...
• Exposure to polluted air is a risk factor
of asthma attack within X hour?
Personicle case
Environmental
factors
Spicy Indian food and 2 glasses of wine
result in severe acidity and sleepless nights.
Personicle
Food Stream
t1 t2 t3 t4 ...
40
Act Orient
Observe
Decide
Decision Making in
Complex Situations
John Boyd
Military Strategist
OODA
Situation awareness is knowing
what’s going on around you.
41
Observe + Orient = Situational Awareness
Orient: Baselines, ...
Dashboards Display Data and Information
42
Operators use relevant information to understand
situation to decide relevant A...
10/15/2016 43
Calendar PESi
FMB (Individual’s Feeling)
Accelerometer
Location
Fitness Data
(Nike, Fitbit) Data
Ingestion &...
Great Opportunity: Wearable
• Wearable building personal models.
• Using personal model to make people aware
of their situ...
Personal lifestyle and health 161014
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Personal lifestyle and health 161014

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Talk at Wearable 2016 Symposium in Lausanne.
This presentation talks about use of wearables and other sensors for quantifying lifestyle and relating it to build model of personal health.

Published in: Data & Analytics
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Personal lifestyle and health 161014

  1. 1. 10/15/2016 1
  2. 2. Society exists only as a mental concept; in the real world there are only individuals. -- Oscar Wilde
  3. 3. Wearables: Watches, camera, … I don’t care for money; I care for what money buys. I don’t care for wearables; I care for what wearables do for me.
  4. 4. Disruption in Healthcare When needed, I go to the source of healthcare. Can healthcare come to me in time?
  5. 5. Event Mining Machine Learning Individual Model ‘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
  6. 6. Important Revolution in Health In the mid-20th century, the primary causes of death worldwide shifted from infections to chronic conditions.
  7. 7. Dream: Immortality Problem: Longest people can live is 122 Yrs. Realizable Dream: 100+ Years of Happiness
  8. 8. Personal Health What’s Lifestyle got to do with it?
  9. 9. High Blood Pressure Heart Disease Allergies Thyroid Imbalance Diabetes Chronic Fatigue Auto Immune Disease Cancer Stress Poor Diet Lack of Sleep Lack of Exercise Nutrition Deficiencies Genetics Toxins
  10. 10. Needed: Medical Emancipation Doctor Knows the Best. Patient Knows the Best.
  11. 11. Smartphone + Wearables := Personalized 24/7 Recording Stethoscope
  12. 12. Financial Physical Environ- mental Emotional Sexual Social Intellectual Spiritual Lifestyle
  13. 13. Big 3: Lifestyle Factors Physical Activities Popular now: Output and state Food Starting to happen: Input Environmental Factors Coming soon: Surround
  14. 14. Healthcare Analytics is Data Rich Now Past: Data is expensive and small • Input data is mostly from clinical trials • Models are small since data is limited • Personal information is anecdotal Today: Data is cheap and large • Longitudinal data • Heterogeneous data • Diverse data from Electronic Health Records and wearable/smartphone
  15. 15. Until recently, you were a folder. Now You are Your Data. Disruption Time
  16. 16. “Shallow men believe in luck or in circumstance. Strong men believe in cause and effect.” ― Ralph Waldo Emerson
  17. 17. If you can't measure it, you can't control it! Measure Understand Control Improve
  18. 18. OBJECTIVE SELF (FUTURE WORK) OBJECTIVE SELF Anecdotal Diarizing data Quantified Self
  19. 19. Data Streams to Objective Self Daily Life Activities
  20. 20. Data Streams Data Variety: From Data Streams to Abstracted Event Streams Data Management Event Streams 20
  21. 21. Event Stream
  22. 22. DailyLife Activity ATUS: American Time Use Survey
  23. 23. Daily Life Activities: How we enter them? Chronicle of Daily Life Activities
  24. 24. We collect diverse signals.
  25. 25. Running Example 25 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 Framework Heart rate Events NormalHigh Elevated
  26. 26. 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]) 26 Life Events WalkingMeeting Break Arrive Home Exercise Asthma Attack 91 4 6 10 15 197 14 Leave Home Framework
  27. 27. 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 , , ∣ } 27 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) Framework
  28. 28. Representations Laleh Jalali lalehj@ics.uci.edu Interactive Knowledge Discovery from Data Streams 28 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) Framework
  29. 29. Pattern Mining Operators Sequential Co-occurrence Concurrent Co-occurrence 29 Laleh Jalali lalehj@ics.uci.edu Interactive Knowledge Discovery from Data Streams Framework
  30. 30. 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 30
  31. 31. 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:
  32. 32. 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
  33. 33. User Interface for Interactive Knowledge Discovery and Model Building Data-Driven Hypothesis- Driven
  34. 34. Pollution and Meteorological Data
  35. 35. 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.
  36. 36. 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.
  37. 37. 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.
  38. 38. • Exposure to polluted air is a risk factor of asthma attack within X hour? Personicle case Environmental factors
  39. 39. Spicy Indian food and 2 glasses of wine result in severe acidity and sleepless nights. Personicle Food Stream t1 t2 t3 t4 t5
  40. 40. 40 Act Orient Observe Decide Decision Making in Complex Situations John Boyd Military Strategist OODA
  41. 41. Situation awareness is knowing what’s going on around you. 41 Observe + Orient = Situational Awareness Orient: Baselines, Goals, and Action Plans Situation awareness is required for every decision in life. Act Orient Observe Decide
  42. 42. Dashboards Display Data and Information 42 Operators use relevant information to understand situation to decide relevant Action.
  43. 43. 10/15/2016 43 Calendar PESi FMB (Individual’s Feeling) Accelerometer Location Fitness Data (Nike, Fitbit) Data Ingestion & Aggregation Heart Rate Location (Move) Food Log FMB (People’s Feeling, Location) ESOzone CO2 SO2 PM 2.5 Pollen (Tree, Grass) Air Quality Index Data Ingestion & Aggregation Social Media (News, Tweets) Weather Macro Situation Recognition Predictive Analytics Personal Situation Recognition Persona Asthma Allergy App Server Data Collection MacroSituationPersonalSituation Need and Resources Recommendation
  44. 44. Great Opportunity: Wearable • Wearable building personal models. • Using personal model to make people aware of their situation. • Informing relevant people about the situation. • Helping people make Right Decision, Right Moment, Right Place.

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