Healthcare.AI is found at https://healthcare.ai/ Healthcare.ai is an open source machine learning toolkit for healthcare. It has a dedicated team of healthcare data scientists, and is part of Health Catalyst (https://www.healthcatalyst.com). Presentation given by Dan Wellisch, organizer of the Chicago Technology For Value-Based Healthcare (https://www.meetup.com/Chicago-Technology-For-Value-Based-Healthcare-Meetup/) on 10/30/2017.

1. HealthCare.AI (Python version) based on Python sk-learn library
Chicago Technology For Value-Based Healthcare
https://www.meetup.com/Chicago-Technology-For-Value-Based-Healthcare-Meetup/
10/30/2017
Dan Wellisch, Meetup Organizer
Healthcare.AI is found at https://healthcare.ai/ Healthcare.ai is an open source machine
learning toolkit for healthcare. It has a dedicated team of healthcare data scientists, and
is part of Health Catalyst (https://www.healthcatalyst.com).

2. • The Healthcare.AI toolkit also has an R version.
• Data Input: Feature Columns
• Data Output: Prediction Column
• Types Of Problems
Classification – Binary or Multiclass
Regression – Numeric
• Classification Example: Predicting 30 Day Readmission for
Diabetes Patients
Introduction

3. Training the Model
1. Setup a pipeline. Each “pipe” in the pipeline is either a transformer or estimator.
A transformer implements a fit and transform method. An estimator implements a
fit and predict method.
transformer estimator
2. Clean the data by processing through the pipeline.

4. Training the Model
3. Split data into train and test sets.
4. Train a model. Example models to train are the following.:
a) linear regression model
b) logistic regression model
c) lasso regression model
d) ensemble regression model
e) ensemble logistic model
f) random forest regression model
g) random forest classification model
h) knn model
5. Tweak model parameters for models you investigate on your
training set. Go back to step 4 until you have found your best
performing model on your training set.
4. Save your best performing model (for your training set).
5. The saved model can now be put into production.
Demo Model Training
Execute example_1_classification.py

5. Using the Saved Model To Predict Classes
Demo Model Prediction
Execute example_2_classification.py