Beyond Research Using data to empower and engage individuals.
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Transcript of Beyond Research Using data to empower and engage individuals.
Beyond ResearchUsing data to empower and engage
individuals
What happens between physician visits?
JANJANJANJAN FEBFEBFEBFEB MARMARMARMAR APRAPRAPRAPR MAYMAYMAYMAY JUNJUNJUNJUN JULJULJULJUL AUGAUGAUGAUG SEPSEPSEPSEP OCTOCTOCTOCT NOVNOVNOVNOV DECDECDECDEC
Life… How do my daily activities impact my IBD?
Could “observations of daily living” help
individuals:•Better understand their disease
•Develop insights into their own health
•Learn from the experiences of others
•Improve communications with their physicians
•Understand how daily activities impact their own health
•Is collecting all of this data worth the trouble?
•How does my diet and daily activities impact my IBD?
•What is the single best thing I can do today to feel better?
JANJANJANJAN FEBFEBFEBFEB MARMARMARMAR APRAPRAPRAPR MAYMAYMAYMAY JUNJUNJUNJUN JULJULJULJUL AUGAUGAUGAUG SEPSEPSEPSEP OCTOCTOCTOCT NOVNOVNOVNOV DECDECDECDEC
Engaging and empowering Individuals
Nutrition
Sleep
EHR
Activity
Workouts IBD Flares
Beth
Nutrition
Sleep
EHR
Activity
Workouts
IBD Flares
SarahNutriti
on
Sleep
EHR
Activity
Workouts
IBD Flares
Bill
SleepActivity
Compare your own activities to your own outcomes.
CRPFlaresDiet
Goal
Track activities and outcomes across groups.
Fell BetterFell Better
Actively Manage Flare-ups
Actively Manage Flare-ups
Reduced Frequency & Length of Flare-
ups
Reduced Frequency & Length of Flare-
ups
Take Less MedsTake Less Meds
RemissionRemission
YOUYOUYOUYOU
There are thousands of deliberate and accidental experiments being
conducted every day…
Fell BetterFell Better
Actively Manage Flare-ups
Actively Manage Flare-ups
Reduced Frequency & Length of Flare-
ups
Reduced Frequency & Length of Flare-
ups
Take Less MedsTake Less Meds
RemissionRemission
YOUYOUYOUYOU
How can we systematically learn from these interactions?
Machine LearningApplications to Inflammatory Bowel Disease
Seth Myers, PhD Candidate, Computational Mathematics at Stanford University. Prior to Stanford Seth received his BA from
Northwestern University, where he triple-majored in Math, Computational Physics and Integrated Science.
Machine Learning
• Software model that learns by example
• We give the model a hard task
• Maybe we have only partial or no knowledge of task
• Maybe there are too many moving parts to
comprehend
• We show the model many examples of the task
• It learns to do the task on it’s own
Machine Learning is Everywhere• Fraud detection for your credit card
• Trained on many examples of fraud
• Learns to recognize suspicious activity
• Search Engine Results
• Trained on what people clicked on in the past
• Predicts what result is most relevant to you
• Mail sorting at the post office
• Trained on many addressed envelops
• Learns to read all types of handwriting
Machine learning and IBD
•Several potential applications
•Predict future flares or episodes for a patient
•Predict intensity or duration of future flares
•Generate recommendations to reduce risk
Learning IBD by exampleTraining examples: A group of IBD
patients
Some patients experienced a flareThe model learns to recognize patients right
before flares
? The Model
21.5% chance of future flare
How it Works• Example: a Decision Tree
• Let’s say training set of 10,000 patients, 30% with flares
• Model asks questions about data that best split flares from non-flares
• Each step finds best question to identify future flare patients• Does the patient smoke?• Has the patient been getting more sleep?• Higher C-Reactive Protein than normal?• Has the patient been consuming more fiber?
Does the patient
smoke?Has the patient been getting more sleep?Higher C-Reactive Protein than normal?Has the patient been consuming more fiber?
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Combining Many Decision Trees•One decision tree works OK
•Many decision trees working together is very accurate
•Each tree is trained on the same set of patientsThe trees are built one at a
time
Each tree pays more attention to mistakes of
previous trees
Emphasizing different patients means different
perspectives on same task
What can we do with the model?•For physicians
•Alerts when patients become high risk
•For scientific understanding
•Many unanswered questions about environment/behavior and IBD
•Can the model find unknown correlations?
•For patients
•What small behavior changes will reduce my risk?
For the Patient, by the Patient
•The model can only be as good as the data
•The more the patient uses the Portal, the more accurate his/her recommendations
•The more the patient recruits other patients, the more accurate his/her recommendations