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In digital health #wwdh @neal_Lathia DATA SCIENCE
36

Data Science in Digital Health

Apr 21, 2017

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Neal Lathia
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Page 1: Data Science in Digital Health

In digital health#wwdh @neal_Lathia

DATASCIENCE

Page 2: Data Science in Digital Health

SOCIAL

SCIENCE

DATA

SCIENCE

HUMAN

COMPUTER

INTERACTION

BEHAVIOUR

CHANGE?

Page 3: Data Science in Digital Health

UNDERSTAND AUTOMATE

DESIGN

BEHAVIOUR

CHANGE?

Page 4: Data Science in Digital Health

HOW DOES

BEHAVIOUR

CHANGE?

HOW COULD

TECHNLOGY

INTERACT WITH

PEOPLE?

HOW Do PEOPLE

INTERACT WITH

TECHNLOGY?

BEHAVIOUR

CHANGE?

Page 5: Data Science in Digital Health

DATA

SCIENCE

HUMAN

COMPUTER

INTERACTION

DIGITAL

BEHAVIOUR

CHANGE

Page 6: Data Science in Digital Health

MAKING

CHOICES

CASE 1: memory & choice (NOT HEALTH)

Page 7: Data Science in Digital Health

“Psychologists have recognized for many

years that humans have a limited capacity to

store current information in memory.”- “Information Overload” on Wikipedia

Page 8: Data Science in Digital Health

SURROUNDED BY CHOICES

Page 9: Data Science in Digital Health

AUTOMATED BY RECOMMENDATION

- Neal's slides during his PhD

Page 10: Data Science in Digital Health

AUTOMATED BY RECOMMENDATION

- Neal's slides during his PhD

Navigating choice ~Predicting missing dataRanking on predictions

Page 11: Data Science in Digital Health

AUTOMATED BY RECOMMENDATION

- Neal's slides during his PhD

No “framework”No “item” context

No theory/categorisationSimplistic assumption

No uniformity1000 outcomes for 1000 people

Page 12: Data Science in Digital Health

USES BEHAVIOURAL THEORY

Online Recommendations

EXPLAINS THE BEHAVIOUR

ALWAYS GETS IT RIGHT

AUTOMATED PROCESS

ENHANCES ENGAGEMENT

CHANGES BEHAVIOUR

NO

NO / BADLY

NO

YES

YES

YES

Page 13: Data Science in Digital Health

USES BEHAVIOURAL THEORY

EXPLAINS THE BEHAVIOUR

ALWAYS GETS IT RIGHT

AUTOMATED PROCESS

ENHANCES ENGAGEMENT

CHANGES BEHAVIOUR

NO

NO

NO

YES

YES

YES

DOMAIN

KNOWLEDGE

DATA

SCIENCE

BOTH

Online Recommendations

Page 14: Data Science in Digital Health

“Your decades of specialist knowledge are not

only useless, they're actually unhelpful; your

sophisticated techniques are worse than

generic methods; The algorithms tell you

what's important and what's not...”

- @jeremyphoward (Interview)

Page 15: Data Science in Digital Health

“...You might ask why those things are

important, but I think that's less interesting.

You end up with a predictive model that

works.”

- @jeremyphoward (Interview)

Page 16: Data Science in Digital Health

SOCIAL SCIENCE...?

Page 17: Data Science in Digital Health

WHAT SMARTPHONES CAN

SENSE THEMSELVES

What SMARTPHONES CAN

PROMPT YOU TO TELL

The Emotion Sense Platform:

Location, mobility, sociability, physical activity

Mood, symptoms, assessments

Page 18: Data Science in Digital Health
Page 19: Data Science in Digital Health

QUITTING

SMOKING

CASE 2: Automating support

Page 20: Data Science in Digital Health

YOUR SMOKING BEHAVIOUR

Smoking Cessation – Ideal

+ “ReCOMMENDED” SUPPORT

= BEHAVIOUR CHANGE

Page 21: Data Science in Digital Health

YOUR SMOKING BEHAVIOUR

Smoking Cessation – Ideal

+ “RECOMMENDED” SUPPORT

= BEHAVIOUR CHANGE

NO DATA ON THE “USER”

WHAT IS THE “ITEM?”

NOT POSSIBLE?

Page 22: Data Science in Digital Health

“Cold start is a potential problem in

computer-based information systems (...WHERE..)

the system cannot draw any inferences for

users (or items) about which it has not yet

gathered sufficient information.”

- “Cold Start” on Wikipedia

Page 23: Data Science in Digital Health

- “Cold Start” on Wikipedia

“Cold start is a potential problem in

computer-based information systems (...WHERE..)

the system cannot draw any inferences for

users (or items) about which it has not yet

gathered sufficient information.”

And beyond: in a given health domain, what information

should we (can we) collect?

Page 24: Data Science in Digital Health

HEALTH

/SOCIAL

SCIENCE

DATA

SCIENCE

HUMAN

COMPUTER

INTERACTION

DIGITAL

BEHAVIOUR

CHANGE

Cold start

Page 25: Data Science in Digital Health

“cue-induced cravings: intense, episodic cravings

typically provoked by situational cues

associated with drug use (...) smokers exposed

to smoking-related cues demonstrate

increased craving (...).”

- Ferguson, Shiffman. The relevance and treatmentof cue-induced cravings in tobacco dependence. In JSubst Abuse Treat. April 2009.

Page 26: Data Science in Digital Health

“cue-induced cravings: intense, episodic cravings

typically provoked by situational cues

associated with drug use (...) smokers exposed

to smoking-related cues demonstrate

increased craving (...).”

- Ferguson, Shiffman. The relevance and treatmentof cue-induced cravings in tobacco dependence. In JSubst Abuse Treat. April 2009.

Situation: mood, craving,location, social setting

Page 27: Data Science in Digital Health
Page 28: Data Science in Digital Health

Your location + your profile = tailored support

EXAMPLE

Page 29: Data Science in Digital Health

USES BEHAVIOURAL THEORY

EXPLAINS THE BEHAVIOUR

ALWAYS GETS IT RIGHT

AUTOMATED PROCESS

ENHANCES ENGAGEMENT

CHANGES BEHAVIOUR

YES

NO

NO

YES

YES?

YES?

Smoking Cessation

YES

(BUT what DATA!)

Page 30: Data Science in Digital Health
Page 31: Data Science in Digital Health

GOING

FORWARD

AND FINALLY:

Page 32: Data Science in Digital Health

UNDERSTAND IMPLEMENT EVALUATEDesignAutomate

HYPOTHESIS

Linear/hypothesis driven research: good forpublication, bad for software.

Page 33: Data Science in Digital Health

MONITOR LEARN

DELIVER

N. Lathia et. al. In IEEE Pervasive Computing. 2013.

SOFTWARE IS NEVER FINISHED...

... IT IS UPDATED.

HYPOTHESIS

Page 34: Data Science in Digital Health

UNDERSTAND AUTOMATE

DESIGN

BEHAVIOUR

CHANGE?

Page 35: Data Science in Digital Health

SCHIZOPHRENIA

ANXIETY

MOOD ADJUSTMENT

ANTI-SOCIAL PERSONALITY

ON/oFFLINE MOOD EXPRESSION

FREEMIUM

Code: http://emotionsense.github.io/

Page 36: Data Science in Digital Health

In digital health#wwdh @neal_Lathia

DATASCIENCE