Hands-on Machine Learning Using Healthcare
Levi Thatcher, Health Catalyst Director of Data Science and his team provide a live demonstration using healthcare.ai to implement a healthcare-specific machine learning model from data source to patient impact. Levi goes through a hands-on coding example while sharing his insights on the value of predictive analytics, the best path towards implementation, and avoiding common pitfalls. Frequently asked questions are answered during the session. During the webinar, we will: Describe and install healthcare.ai Build and evaluate a machine learning model Deploy interpretable predictions to SQL Server Discuss the process of deploying into a live analytics environment. If you’d like to follow along, you should download and install R and RStudio prior to the event. We look forward to you joining us!
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Hands-on Machine Learning Using Healthcare
- 2. Health Catalyst Data Science Team 2 Levi Thatcher Mike Mastanduno Taylor Larsen Taylor Miller
- 3. Purpose of today’s chat 3 • Explain what healthcare.ai does • Get you started in RStudio • Talk about the roadmap • Q&A
- 4. What does healthcare.ai do? 4 • Enables quick model creation and deployment • Common pre-processing functions • Proper algorithms for healthcare • Suitable metrics for model evaluation • Easy deployment for nightly prediction
- 5. Who is healthcare.ai for? 5 • Technical folks • Business intelligence folks • Data scientists
- 6. Poll question – Operating System 6 At work, what operating system would you use for R/Python? 510 respondents • Windows – 79% • Mac – 11% • Linux – 11%
- 7. Poll question – R vs Python 7 Have you ever run R or Python code before? 526 respondents • R – 22% • Python – 14% • Neither – 36% • Both – 28%
- 8. Algorithm choices for healthcare.ai 8 • Lasso • Random Forest
- 9. Difference between R and Python packages? 9 • R package currently has more functionality • Python will be used to leverage large datasets *Image from page 9 of Andrew Ng’s mlyearning.org *
- 10. Workflow of deploying a model using healthcare.ai? 10 • Combine features with the two algorithms and assess model performance, iteratively • Deploy the model with the features and algorithms that worked best
- 11. Poll question – I/O 11 What type of data connections would you primarily use for R? 405 respondents • CSV – 26% • SQL Server – 51% • MySQL – 9% • Postgres – 3% • Oracle – 11%
- 12. I/O for healthcare.ai 12 • Databases via SQL Server • CSV, TXT, etc
- 13. healthcare.ai use cases 13 • 30-day readmissions • Hospital acquired infections like CLABSI • No-shows, propensity to pay, census, etc
- 14. Examples! 14
- 15. Roadmap 15 • Submit to CRAN • Submit to PyPI • Switch to MySQL as default database • Add deep learning into python package
- 16. Want to contribute? 16 • Clone our repos!
- 17. Poll question – What’s impeding you? 17 What is impeding you from using healthcare.ai? 277 respondents • Loading data into R – 8% • Installing the package – 15% • Don’t know how to integrate into database infrastructure – 31% • Adoption – clinical team isn’t interested – 7% • Not sure what to predict – 38%
- 18. Before we end… 18 • healthcare.ai is our public offering—it’s currently being integrated into HC’s products • Check out the blog • File issues on Stack Overflow with the package version number and tag ‘healthcare-ai’
- 19. Questions? 19