Beyond ResearchUsing data to empower and engage

individuals

What happens between physician visits?

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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?

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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