Presentation covers using data to improve people's health with two examples: mining social media and claims data,. Presentation to The Innovative Member Engagement Conference, Oct 22, 2015 Las Vegas

1. Machine Learning and
Patient Engagement
@timgilchrist

2. Big Data
• Many data sources with different formats
• Data with missing values
• Text / Social Media
• Things that don’t fit in Excel
The term for a collection of data sets so large and
complex that they become difficult to process

3. Artificial Intelligence
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“Pay no attention to the man behind the curtain”

4. Machine Learning
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The construction and study of systems that can learn
from data

5. Where did it all Start? Bayes
Thomas Bayes (1701 – 7 April 1761)was an English mathematician
and Presbyterian minister, known for formulating the theorem
that bears his name: Bayes' theorem
• Bayes theorem uses prior probabilities, combined with new
observations to calculate the probability of a hypothesis being true or
false
• Bayes is a natural fit to health care due to the presence of hypothesis
(diagnosis) and events (tests / observations)
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6. Bayes Example
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33% 33% 33%10% 80% 10%

7. How can we Apply Machine Learning in
Healthcare?
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Identify patterns that humans have trouble seeing
Population
Health
Care
Optimization
Precision
Medicine
R&D
Productivity

8. What Does this Mean To Patient
Engagement?
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You are Here

9. Regression
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Cost
ER Visits

10. Classification
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Orthopedic
Chest Pain
Abdominal Pain
Cost
ER Visits

11. Example
Social Media Text Mining / Diabetes
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12. Twitter Users Self-Described Diabetics
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•What if you could identify “real” diabetics on twitter?
• You could engage them in diabetes education, etc.
• Cost = $0
• Know things that don’t show up in claims (latency)
• Possibly alert the undiagnosed
“Lets play a game called how many times
will my relatives ask about my diabetes.
#byyyyeeee”

13. Results
• 73.5% Accuracy (ability to identify self-
described diabetics from spam, people
mentioning other people’s diabetes,
retweets, bots, etc.)
•Variables in order of importance
• #times others favorited tweets
• #followers
• #user statues
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14. Results / Decision Tree
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# Favorites
# Followers
# Statuses
True (48% / 2%)
<=226 >226
True (44% / 21%)
<=1903 >1903
True (6% / 0%)
<=65.5

15. Example
MI Patients not Taking Beta Blockers
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16. MI Patients not Taking Beta Blockers
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•What patterns exist in this population?
• You had a heart attack but not taking beta
blockers
• What can we learn to effectively reach these
people
• Are they homogeneous or are there sub groups
• Preemptive activities?

17. Results
• 74% Accuracy (ability to predict
compliance with rule – take beta blockers)
•Variables in order of importance
• #primary diagnoses
• #Evaluation & Management visits
• Prior compliance with other rules
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18. Results / Decision Tree
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NC = 68%
SC = 25%
UC = 6%
100%
1,2 0
NC = 42%
SC = 46%
UC = 12%
35%
NC = 82%
SC = 14%
UC = 3%
64%
#primary diagnoses
< 1 >= 2
NC = 86%
SC = 13%
UC = 0%
8%
NC = 29%
SC = 55%
UC = 15%
8%
E&M Visits
NC = Never Compliant
SC = Sometimes Compliant
UC = Usually Compliant