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Machine Learning and Patient Engagement
@timgilchrist
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
Artificial Intelligence
3
“Pay no attention to the man behind the curtain”
Machine Learning
4
The construction and study of systems that can learn from data
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)
5
Bayes Example
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33% 33% 33%10% 80% 10%
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
What Does this Mean To Patient Engagement?
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You are Here
Regression
9
Co
st
ER Visits
Classification
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OrthopedicChest PainAbdominal Pain
Co
st
ER Visits
ExampleSocial Media Text Mining / Diabetes
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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”
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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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
ExampleMI Patients not Taking Beta Blockers
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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?
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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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 CompliantSC = Sometimes CompliantUC = Usually Compliant