machine learning in healthcare -...
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Machine Learning in HealthcareND HIMSS Spring 2017 Conference
Fargo, ND
April 12, 2017
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AI Quiz
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AI Quiz
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AI Quiz
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Objectives
Learn some buzzwords
Why Bother?
How to build a predictive model
Examine real-world predictive models
Getting Buy-In from Clinicians
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AI: Artificial Intelligence
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General Artificial Intelligence
“Narrow” Artificial Intelligence
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Machine Learning
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Machine learning explores the study and
construction of algorithms that can learn from
and make predictions on data.
https://en.wikipedia.org/wiki/Machine_learning
Predictive analytics, or making predictions
based on past data, is one of the artificial
intelligence tasks that machine learning can
solve.
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Artificial Intelligence tries to replicate the capabilities of the human
mind.
Machine Learning uses complex math to solve difficult problems.
Predictive Analytics, from the standpoint of healthcare or business,
is one of the most important activities that is enabled by Machine
Learning.
Predictive Models and Risk Models are the products of Predictive
Analytics.
I’m still confused…
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Why bother?
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Classic Approaches
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Mortality prediction
The Charlson Index was introduced in
1987 in the Journal of Chronic Disease as
mortality risk score.
Readmission prediction
The LACE Index was introduced in the
Canadian Medical Association Journal in
2010 to predict early death or unplanned
readmission after discharge.
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Shortcomings…
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Using the LACE index
to predict hospital
readmissions in
congestive heart failure
patients
By Wang et. al, BMC Cardiovascular
Disorders , 2014
Predicting
readmissions: poor
performance of the
LACE index in an older
UK population
By Cotter et al., Age Aging , 2012
CONCLUSION: The LACE Index may not accurately predict unplanned
readmissions within 30 days from hospital discharge in CHF patients. The
LACE high risk index may have utility as a screening tool to predict high risk
ED revisits after hospital discharge.
CONCLUSION: The LACE Index is a poor tool for
predicting 30-day readmission in older UK inpatients.
the absence of a simple predictive model may limit
the benefit of readmission avoidance strategies.
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Most standard models are trained with data from a broad, general
population.
Most standard models are based upon data elements that are
available through billing or claims data.
Limitations
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Trained on data from your environment.
Trained on data from your patients.
Answers your specific questions.
Advantages of building models
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Trying to differentiate outcomes for complex cohorts
Predict infrequent events
Prioritize attention of limited resources to very frequent events
Predict outcomes as the result of modified behaviors
Incorporate features unlikely to be available to “standard” models
- Socio-economic data
- Geo-location data
When should I build a model?
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Let’s Try It
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Let’s Build a Predictive Model
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Steps to build a model
1. Determine event of interest.
2. Determine our population.
3. Decide upon “features.”
4. Build feature sets.
5. Run through various algorithms: Train and Test.
6. Select the best model.
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Typical Workflow for Building a Predictive Model
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Data Source
Feature
Set
Gnarly SQL Query
Data Manipulation
Tools/Algorithms
SAS | Weka |
R | Python
Evaluate
&
Select
Best
Candidate
Models
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Features
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Delivery Date Delivery Location Humour Temperament Blood Letting Physician Type Hand Washing Died
1/1/1844 Clinic 1 Sanguine Yes Physician Yes No
1/1/1844 Clinic 1 Melancholy No Physician No Yes
1/1/1844 Clinic 1 Balanced No Physician No No
1/1/1844 Clinic 2 Choleric No Midwife Yes No
1/1/1844 Clinic 2 Phlegmatic No Midwife Yes No
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Training and Testing
Most records will be used to “train” or create the models.
The remaining records will be used to test, or determine the
accuracy, of each model.
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Algorithm 1 Algorithm 2 Algorithm 3
Model &
Accuracy
Report
Model &
Accuracy
Report
Model &
Accuracy
Report
Model &
Accuracy
Report
Model &
Accuracy
Report
Model &
Accuracy
Report
Model &
Accuracy
Report
Model &
Accuracy
Report
Features (i.e. age, comorbidities, polypharmacy)
Result:
• Handful of best (most
predictive) features
• Best algorithm that
computes the relationships
between input features to
generate prediction
• Performance report
summarizing best ‘model’
Algorithms (i.e. Lasso, Random Forest, k-means)
Definition: Simply put, a feature is an input to a machine learning model
Definition: Algorithms are complex mathematical processes that
discover the relationship between features (input) and the
outcome being predicted.
Developing a Predictive Model
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When Delivery Location = Clinic 1 and Hand Washing = No, women
are 3 times more likely to die. Humours are not predictive, and blood
letting correlates slightly with death.
Dr. Semmelweis’s Model
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Real World Models
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Real World Use Case: COPD Readmissions
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From nih.gov
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Can we develop a model to help Pulmonary Navigators identify which
COPD patients are most likely to experience an exacerbation that
would lead to a readmission?
COPD Readmission Challenge
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Total number of respiratory disease index admissions: 90,312
Total number of features: 29
Final number of features used: 19
COPD Model Example
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COPD Model Example
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COPD Readmissions
Note: Data is from de-identified data set and in some places fabricated in order to show a reasonable representation of actual trends
and observations from production data. All names, addresses, and other PHI are fabricated.
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Likelihood of No Shows
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Likelihood of No Shows
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CLABSI
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CLABSI
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Get Buy-In
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“My patients are sicker.”
“You have a FALSE POSITIVE rate of what?”
Getting Buy-In from Clinicians
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Tips for Getting Buy In from Clinicians
If you cannot explain the algorithm, do not use it. Use a simpler
algorithm that you can explain.
#1 Clinicians need to understand the model
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Tips for Getting Buy In from Clinicians
Documentation for any interested stakeholder to learn about the
model:
- Why was it created?
- What features were tried? Which were used?
- What algorithm was used?
- How accurate is the model?
#2 Build a “model performance report”
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Tips for Getting Buy In from Clinicians
#3 Provide details to end users
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Tips for Getting Buy In from Clinicians
#4 It’s just a suggestion
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“Suggestive Analytics” may be a better term than “Predictive Analytics”
to demonstrate that we are not trying to replace human judgement.
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Review
Useful vocabulary for discussing predictive analytics
Usefulness of custom predictive models
The steps to build a predictive model
Examples of how predictive analytics has been deployed in the wild
Tips for getting buy-in from clinicians
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Getting Started
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You Need Smart People!
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• Develops software to
automate machine
learning workflow
• Requires data science
knowledge
• Requires knowledge of
software engineering best
practices
• A rare find!
• Formulates hypotheses
about features driving a
predictive model (with
clinical input)
• Tries various algorithms
to determine best
approach for prediction
• Assesses model output
and accuracy and
operationalizes the best
approach
Machine Learning
EngineerData Architect (Engineer)Data Scientist
• Finds and provisions
source data
• Leverages definitions in
analytics environment
• Feature engineering
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healthcare.ai Open Source Software
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Our open-source
machine learning
software product
Automates key tasks
in developing
models, or
customizing existing
models using local
data
Makes deployment
in an analytics
environment easy
and ‘production
quality’
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