This document discusses the use of big data, artificial intelligence, and social media data in healthcare and diabetes management. It presents research that was able to predict medical diagnoses from language on social media and identifies markers of disease. It also discusses tools that use AI and case-based reasoning to provide insulin dosing recommendations for type 1 diabetes patients based on similar past cases and temporal patient data. The document notes both the promise and limitations of AI in healthcare and that AI will likely require human oversight rather than replacing physicians.

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