10/31/2016 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 what wearables do for me.

Disruption in Healthcare

When needed, I go to

the source of

healthcare.

Can healthcare come

to me in time?

Event MiningMachine 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

Important Revolution in Health

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

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 FatigueAuto Immune Disease

Cancer

StressPoor Diet

Lack of Sleep

Lack of ExerciseNutrition Deficiencies

GeneticsToxins

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

FoodStarting to happen:Input

Environmental Factors

Coming soon: Surround

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

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

Daily Life

A

ctivity

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 EventsWalkingMeeting Break A

rriv

e H

om

e

Exercise Asthma Attack

Pollution Events

91 4 6 10 15 19

Low

Temperature Events

Increase Suddenly High

Decrease SteadilyMedium Low

7 14

Leav

e H

om

e

Framework

Heart rate Events

NormalHigh Elevated

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 EventsWalkingMeeting Break A

rriv

e H

om

e

Exercise Asthma Attack

91 4 6 10 15 197 14

Leav

e H

om

e

Framework

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 EventsWalkingMeeting Break A

rriv

e H

om

e

Exercise AsthmaAttack

91 4 6 10 15 197 14

Leav

e H

om

e

( Meeting ; Break)

( Exercise+ ;ω[5] AsthmaAttack)

( Walking ; ArriveHome)

Framework

Representations

Laleh Jalali lalehj@ics.uci.edu

Interactive Knowledge Discovery from Data Streams

28

Daily EventsWalkingMeeting Break A

rriv

e H

om

e

Exercise Asthma Attack

Pollution Events

91 4 6 10 15 19

1

Low

8 10

24

Temperature Events

Increase Suddenly High

1

Decrease SteadilyMedium Low

247 14

7 14

Leav

e H

om

e

(( Exercise+ Pollution.High) ;ω[5] AsthmaAttack)

(( Exercise+ Temp.Low ) ;ω[5] AsthmaAttack)

(( Exercise+ (Pollution.High | Temp.Low )) ;ω[5] AsthmaAttack)

Framework

Pattern Mining Operators

Sequential Co-occurrence

Concurrent Co-occurrence

29Laleh Jalali lalehj@ics.uci.edu

Interactive Knowledge Discovery from Data Streams

Framework

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

ΔtE1 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

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 attackΔt

Formulate and query complex patterns:

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

User Interface for Interactive Knowledge

Discovery and Model Building D

ata-

Dri

ven

Hyp

oth

esis

-

Dri

ven

Pollution and Meteorological Data

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.

ResultsTemperature 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.

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.

• 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 t5

40

Act Orient

Observe

Decide

Decision Making in Complex Situations

John BoydMilitary Strategist

OODA

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

Dashboards Display Data and Information

42

Operators use relevant information to understand situation to decide relevant Action.

10/31/2016 43

Calendar PESi

FMB (Individual’s Feeling)

AccelerometerLocation

Fitness Data(Nike, Fitbit) Data

Ingestion & Aggregation

Heart Rate

Location (Move)

Food Log

FMB (People’s Feeling, Location)

ESOzoneCO2

SO2

PM 2.5

Pollen (Tree, Grass)

Air Quality Index

Data Ingestion & Aggregation

Social Media (News, Tweets)

Weather

Macro Situation Recognition

Predictive Analytics

PersonalSituation Recognition

Persona

Asthma Allergy App Server

Data Collection

Mac

ro S

itu

atio

nPe

rso

nal

Sit

uat

ion

Need and Resources Recommendation

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.