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HealthCare.AI (Python version) based on Python sk-learn library

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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.

Published in: Healthcare

HealthCare.AI (Python version) based on Python sk-learn library

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

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