"Machine learning: identification of diabetes risks and complications - presentation of Adrian Ahne's conclusions of his "Cifre" thesis, developed in the framework of Epiconcept and Inserm CESP - Generation and Health team. Here are summarized methods used to extract text data from social networks (here, Twitter) and a summary of topics covered by people with or related to diabetes on social networks.

1. Identification of diabetes-related distress
patterns based on social media data using
Artificial Intelligence methods:
THE WORLD DIABETES DISTRESS STUDY
Adrian Ahne, Cifre PhD student
Supervisor: Guy Fagherazzi, PhD, HDR
Center of Research in Epidemiology and Population
Health, Inserm & Epiconcept company
EDEG, 11/05/2019

2. Background
2

3. 3
What is Diabetes Distress?
Up to 36%
of adults
with
diabetes
experienced
elevated or
severe
diabetes
distress at any
given time
Associated with
day-to-day
disease
management
Diet
Physical activity
Fear of hypoglycemia
Fear of complications
Work
Family support
HCP relationships...
Stress
Emotional
burden
Fatigue Anxiety

4. PAID
Problem Areas In
Diabetes
Limitations
▹ Self-reported
▹ Non evolutive
▹ Some components of DD are
missing (work-related issues, cost
of treatment, HCP relationships...)
▹ Interpreted with a HCP
▹ “Make my doctor happy” effect
▹ Risk of denial and bias
4

5. 5
World Diabetes Distress Study (WDDS)
= +
Clinical data
Social media
data
Smartphone
applications
Connected
objects
WDDS objective: Identification of risk factors for diabetes distress, bad
quality of life and risk of diabetes complications using innovative
digital data
Current sub-objective: Identification of diabetes distress patterns all over the
world based on textual data from social media (Twitter)

6. Material & Methods
6

7. 7
➔ Data public by default
➔ > 1000 publications
➔ Strong , active
diabetes community
➔ Collection of 85 000 diabetes related tweets per week since May 2017
(Total: 12 millions of diabetes-related Tweets in English, French and
Spanish)
Why Twitter?
Worldwide occurrence of “#diabetes” on Twitter
high activity
low activity

8. 8
Data pre-processing
Tweets
Personal
Institutional
Spam
No joke
US
tweets
Geo
location
Identification of
diabetes distress
patterns
Prediction model
(Gender / Type of
diabetes)
Unsupervised
Supervised
N = 1.15 M
Joke
Supervised
Supervised
N = 3 M
N = 720.000 N = 164.000
1 2 3
4
5

9. 9
➔ Natural language processing: Word embedding
◆ vectorized representations of words (200 dim)
◆ Intuition: similar words should be close in the vector space
◆ Algorithm to calculate word embedding: FastText
➔ Machine learning
◆ Supervised algorithm: find groups based on labeled data
● Support vector machine (SVM)
◆ Unsupervised algorithm: Detecting clusters based on non-labeled data
● K-means
Methodology

10. 10
Algorithms
Aim Detect personal content
(emotions, feelings, etc.)
Filter out jokes
around diabetes
Predict gender
(male, female, unknown)
Predict the type of
diabetes
(no diab, type1, type2)
Geolocation of tweets on city
level based on meta-data
Training
Dataset
1884 tweets labeled 1000 tweets labeled 1897 tweets labeled
+ 3695 tweets: first name
matched dictionary of
names from SSA (Social
security administration )
1897 tweets labeled - Dictionary city, state, region
information world wide
(geonames.org)
Features Tweets Tweets, user
description
Tweet, user description and
first name in user name
Tweets, user description User location or user description
Algorithm SVM - Index search
- SVM (classify if word is location)
Performances - Accuracy: 85%
- Precision: 94%
- Recall: 71%
- Accuracy: 80%
- Precision: 80%
- Recall: 80%
- Accuracy: 85%
- Precision: 85%
- Recall: 85%
- Accuracy: 75%
- Precision: 77%
- Recall: 75%
- Accuracy: 84%
- Precision: 81%
- Recall: 68%
Comments Precision for a male: 92%
Precision for a female: 93%
Precision type 1: 83%
Precision type 2: 78%
1 2 344

11. Preliminary results
11

12. 12
Distribution of diabetes-related tweets in the USA
Relative occurrence (cities with n> 300 Tweets)
relative number of
tweets in city
(tweets city /
population city)
high tweet density
(Number tweets state
/ state population)
low tweet
density
Number of tweets with
geolocation data
available after
preprocessing: 164.000
Number of distinct
users: 80.000
Las Vegas
Washington
New York
Seattle

13. 13
Topic extraction: FastText + K-means (K=100)
Insulin
(24%)
HCP
relationships
(0.9%)Nutrition
(3.5%)
Feelings about
diabetes
(9.3%)
Diabetes
(10.4%)
Diabetes-related
complications and
comorbidities
(9.7%)
Healthcare
(8.5%)
Diabetes
management
(6.7%)
Entourage
(6.8%)
Diabetes
online
community
(6.8%)
Miscellaneous
(7.3%)
Jokes
(0.3%)
Glucose
tests
(2.9%)Day-to-day
life
(1.9%)
Donations
/ Support
(1.1%)

14. 14
Insulin
(24%)
Insulin
price
Death
without
insulin
Cannot
afford
insulin
Seeking
out help
to access
insulin
Insulin
pump
Glucose
biomec
hanism
Insulin
use
Need
insulin to
survive
Insulin
shots
Biomechanism
Elevated
costs
Insulin
rationing
Insulin
“Can someone please
help me out with $40 I
didn't know that I ran
out of insulin.i really
need it please.i go back
to work tommeow”
Zoom into topics (1/2)
“y’all so like i’m
really out here
rationing out my
insulin bc i can’t
afford my
medication until i
get paid on friday”
“It’s sad when you have to
show your insulin pump
site in order to get needles
to inject insulin.”
“I actually agree w this
one. Its absurd that a
medication as old as
insulin is as expensive
as it is.”
“To think this little
bottle of insulin
saves my life.
Without it, I’d die.
Type 1 Diabetics
need affordable
insulin…”
14

15. 15
Insulin
(24%)
Feelings
about
diabetes
(9.3%)
Insulin
price
Death
without
insulin
Cannot
afford
insulin
Seeking
out help
to access
insulin
Insulin
pump
Glucose
biomec
hanism
Insulin
use
Need
insulin to
survive
Insulin
shots
Biomechanism
Elevated
costs
Insulin
rationing
Insulin
Feeling bad
with diab.
Negative
feelings about
diabetes &
complications
Positive
attitude
towards diab.
Angriness about
other people’s
beliefs/actions
towards diabetes
Gratefulness
Sharing
sympathy
/ pity
Diabetes is
a difficult
disease
Upset
about
diabetes
Feelings
about
diabetes
“Can someone please
help me out with $40 I
didn't know that I ran
out of insulin.i really
need it please.i go back
to work tommeow”
Zoom into topics (2/2)
“y’all so like i’m
really out here
rationing out my
insulin bc i can’t
afford my
medication until i
get paid on friday”
“It’s sad when you have to
show your insulin pump
site in order to get needles
to inject insulin.”
“I actually agree w this
one. Its absurd that a
medication as old as
insulin is as expensive
as it is.”
“To think this little
bottle of insulin
saves my life.
Without it, I’d die.
Type 1 Diabetics
need affordable
insulin…”
“I'm really done
with diabetes. Like
it's just wearing
me out, I can't
sleep, can't eat,
can't see because
it requires so much
work”
“Honestly tired of feeling
like im fucking dying every
morning I absolutely hate
diabetes sucks the fun out
of everything”
“I'm sorry to hear
about your daughter,
Debbie. I have type II
and fortunately I don't
need insulin at this
point.”
“It's crazy how people
won't question diseases
like diabetes, but tell
people to think mental
illness away, or get over it”
“I dislike when people
look at me like I’m
doing something
wrong whenever I do
my insulin”
15

16. Discussion
16

17. 17
Strengths & Limitations
Strengths
- Large number of people living with
diabetes analyzed
- Variability in PWD profiles
- Flexible methodology to identify a
large number of topics of interest,
including those related to diabetes
distress
- No “make my doctor happy” effect
- This approach can capture time trends
in the Diabetes Online Community
Limitations
- PWD who are active on social media
are not representative of all PWD
- But there is a large variability in
the profile -> identify various
clusters
- Precision of our gender and type of
diabetes classifiers are not 100%
- Few clinical and environmental
variables to correlate with for now
(WDDS e-cohort will launch soon)

18. 18
➔ Social media such as Twitter is a useful source to capture information about PWD’s
feelings, emotions, beliefs and fears related to diabetes, diabetes treatment and
diabetes complications
◆ Complementary approach to traditional diabetes epidemiology
➔ PWD in the USA
◆ are afraid of the consequences of the increase of insulin costs
◆ frequently share their emotions and fears about diabetes and its complications
◆ are annoyed by the general confusion between T1D and T2D
Take-away message

19. 19
➔ Feelings, emotions and stress are not well taken into account in diabetes
epidemiology
➔ Future clinical & epidemiological studies should take these factors into account
➔ Intervention studies should focus on reducing the level of stress and fears in PWD
➔ Next steps in WDDS
◆ Develop a Diabetes Distress Score
◆ Study the associations between feelings, emotions and hypoglycemia
◆ Correlate information from Twitter with socio-economic and environmental factors (income, pollution
levels, etc.)
◆ Setup a worldwide online cohort study of people thanks to Diabot (a chatbot)
➔ WDDS is an Open Source project:
https://github.com/WDDS/Tweet-Classification-Diabetes-Distress/
Perspectives

20. 20
Questions?
Acknowledgement Any questions
Thank you Adrahne
adrian.ahne@protonmail.com
Adrian Ahne