Presentation at the 2019 Y4IT Conference, 26 Sept 2019. UP Diliman.

1. BIG DATA, ARTIFICIAL
INTELLIGENCE & HEALTHCARE
Iris Thiele Isip Tan MD, MSc
Professor 3, UP College of Medicine
Chief, UP Medical Informatics Unit
Director, UP Manila Interactive Learning Center

2. NOTHING TO DISCLOSE
I give consent for the audience to tweet this talk
and give me feedback (@endocrine_witch).
Feel free take pictures of my slides (though the
deck will be at www.slideshare.net/isiptan).

3. BIG (social media) DATA
Use of AI in diabetes
Will AI replace physicians?

7. Merchant RA et al. doi.org/10.1371/journal.pone.0215476
Can we predict individuals’ medical diagnoses
from language posted on social media?
Can we identify specific markers of disease
from social media posts?
SOCIAL MEDIA
+
EMR

8. Merchant RA et al. doi.org/10.1371/journal.pone.0215476

9. Facebook alone
Demographics alone
Demographics and Facebook
Medical Condition
Prediction Strength
Merchant RA et al. doi.org/10.1371/journal.pone.0215476
Diabetes!!

10. All 21 medical condition categories were
predictable from Facebook language
beyond chance.
Medical Condition
Prediction Strength
Merchant RA et al. doi.org/10.1371/journal.pone.0215476
18 categories better predicted by
demographics + Facebook language vs
demographics.
10 categories better predicted by
Facebook language vs demographics.

11. Merchant RA et al. doi.org/10.1371/journal.pone.0215476

12. Merchant RA et al. doi.org/10.1371/journal.pone.0215476

13. Merchant RA et al. doi.org/10.1371/journal.pone.0215476

14. Merchant RA et al. doi.org/10.1371/journal.pone.0215476

15. Privacy
Informed Consent
Data Ownership
Merchant RA et al. doi.org/10.1371/journal.pone.0215476

16. Predictive associations of language with
disease may vary across populations
Merchant RA et al. doi.org/10.1371/journal.pone.0215476

17. Use of AI in diabetes
Will AI replace physicians?
BIG (social media) DATA

18. How to calculate insulin
bolus in type 1 diabetes

19. https://mysugr.com/mysugr-bolus-calculator/
Insulin on board
Blood glucose
Carbs
Previous injections
Carbs/insulin ratio
Insulin correction
factor
Blood glucose
target
MySugr Bolus
Calculator

20. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
Advanced Bolus Calculator for Diabetes (ABC4D)
CBR approach: tuning of ISF
and CIR for a small set of meal
scenarios
ISF and CIR from the most
similar case used in a standard
bolus calculator to suggest a
bolus dose
No temporal approach

21. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
Case-based reasoning model for
T1DM bolus insulin advice

22. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
CASE FEATURES
Determine which parameters are
required by bolus calculators
Carbohydrate intake
Pre-meal blood glucose
Target blood glucose level
Insulin-on-board
Exercise
Time
Insulin Sensitivity Factor (ISF)
Carbohydrate-to-Insulin Ratio (CIR)

23. RETRIEVE
Use the date/time of event to infer
ISF and CIR
Factors in preceding bolus doses
REUSE
Adaptation rule which resolves
differences between insulin-on-
board (IOB) in the problem and
retrieved case(s)
Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.

24. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
REUSE step: Average bolus prediction of retrieved
cases then adapt
Equation for averaging bolus prediction of retrieved cases
k = number of retrieved cases
in = bolus solution provided by a retrieved case

25. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
REUSE step: Average bolus prediction of retrieved
cases then adapt
Equations for adapting bolus suggestion

26. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
REVISE
If postprandial BG is equal or
close to target BG then
recommendation is optimal
and not revised

27. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus
insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
Focus on helping patient directly
(instead of aiding the clinician)
RETAINS all
successful cases
Derives bolus
suggestion from
similar cases

28. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin
decision support. Artificial Intelligence in Medicine 2018;85:28-42.
CBR method can be adopted
by insulin pumps, blood
glucose monitors, PCs and
as a web service

29. CBR service in the cloud opens possibility
of case sharing between subjects
Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin
decision support. Artificial Intelligence in Medicine 2018;85:28-42.

30. Use of AI in diabetes
Will AI replace physicians?
BIG (social media) DATA

31. Machine learning represents a
shifting clinical paradigm from rigidly
defined management strategies to
data-driven precision
medicine.
Buch et al. Diabet Med 2018;35:495-7.

33. Buch et al. Diabet Med 2018;35:495-7.
Clinical guidelines will be
delivered through apps
rather than static documents.

34. Buch et al. Diabet Med 2018;35:495-7.
Healthcare professionals will require adequate
training to operate AI-based solutions
Appreciate the limitations of technology
Over-reliance on AI risks de-skilling the profession

35. “The pinnacle of AI is being fully
autonomous. But I don’t think
that will happen in medicine; AI
will always need human
backup.
- Eric Topol MD

36. A robot may not injure a human
being or, through inaction, allow a
human being to come to harm.
A robot must obey orders given it
by human beings except where
such orders would conflict with
the First Law.
A robot must protect its own
existence as long as such
protection does not conflict with
the First or Second Law.
Isaac Asimov’s Three Laws of Robotics