Data Science in Digital Health

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Keynote at "What Works in Digital Health?" #wwdh conference. University of Glasgow, July 2015.

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  • Great presentation. I like how you used venn diagrams :).
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Data Science in Digital Health

  1. 1. In digital health #wwdh @neal_Lathia DATA SCIENCE
  2. 2. SOCIAL SCIENCE DATA SCIENCE HUMAN COMPUTER INTERACTION BEHAVIOUR CHANGE?
  3. 3. UNDERSTAND AUTOMATE DESIGN BEHAVIOUR CHANGE?
  4. 4. HOW DOES BEHAVIOUR CHANGE? HOW COULD TECHNLOGY INTERACT WITH PEOPLE? HOW Do PEOPLE INTERACT WITH TECHNLOGY? BEHAVIOUR CHANGE?
  5. 5. DATA SCIENCE HUMAN COMPUTER INTERACTION DIGITAL BEHAVIOUR CHANGE
  6. 6. MAKING CHOICES CASE 1: memory & choice (NOT HEALTH)
  7. 7. “Psychologists have recognized for many years that humans have a limited capacity to store current information in memory.” - “Information Overload” on Wikipedia
  8. 8. SURROUNDED BY CHOICES
  9. 9. AUTOMATED BY RECOMMENDATION - Neal's slides during his PhD
  10. 10. AUTOMATED BY RECOMMENDATION - Neal's slides during his PhD Navigating choice ~ Predicting missing data Ranking on predictions
  11. 11. AUTOMATED BY RECOMMENDATION - Neal's slides during his PhD No “framework” No “item” context No theory/categorisation Simplistic assumption No uniformity 1000 outcomes for 1000 people
  12. 12. 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
  13. 13. 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
  14. 14. “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)
  15. 15. “...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)
  16. 16. SOCIAL SCIENCE...?
  17. 17. WHAT SMARTPHONES CAN SENSE THEMSELVES What SMARTPHONES CAN PROMPT YOU TO TELL The Emotion Sense Platform: Location, mobility, sociability, physical activity Mood, symptoms, assessments
  18. 18. QUITTING SMOKING CASE 2: Automating support
  19. 19. YOUR SMOKING BEHAVIOUR Smoking Cessation – Ideal + “ReCOMMENDED” SUPPORT = BEHAVIOUR CHANGE
  20. 20. YOUR SMOKING BEHAVIOUR Smoking Cessation – Ideal + “RECOMMENDED” SUPPORT = BEHAVIOUR CHANGE NO DATA ON THE “USER” WHAT IS THE “ITEM?” NOT POSSIBLE?
  21. 21. “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
  22. 22. - “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?
  23. 23. HEALTH /SOCIAL SCIENCE DATA SCIENCE HUMAN COMPUTER INTERACTION DIGITAL BEHAVIOUR CHANGE Cold start
  24. 24. “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 treatment of cue-induced cravings in tobacco dependence. In J Subst Abuse Treat. April 2009.
  25. 25. “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 treatment of cue-induced cravings in tobacco dependence. In J Subst Abuse Treat. April 2009. Situation: mood, craving, location, social setting
  26. 26. Your location + your profile = tailored support EXAMPLE
  27. 27. 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!)
  28. 28. GOING FORWARD AND FINALLY:
  29. 29. UNDERSTAND IMPLEMENT EVALUATE Design Automate HYPOTHESIS Linear/hypothesis driven research: good for publication, bad for software.
  30. 30. MONITOR LEARN DELIVER N. Lathia et. al. In IEEE Pervasive Computing. 2013. SOFTWARE IS NEVER FINISHED... ... IT IS UPDATED. HYPOTHESIS
  31. 31. UNDERSTAND AUTOMATE DESIGN BEHAVIOUR CHANGE?
  32. 32. SCHIZOPHRENIA ANXIETY MOOD ADJUSTMENT ANTI-SOCIAL PERSONALITY ON/oFFLINE MOOD EXPRESSION FREEMIUM Code: http://emotionsense.github.io/
  33. 33. In digital health #wwdh @neal_Lathia DATA SCIENCE

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