Health Catalyst Upcoming Webinar
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Machine Learning Using Healthcare.ai: a Hands-on Learning Session

The purpose of this webinar is to take a tour of Healthcare.ai, a free, predictive analytics platform that is tailored to healthcare, and to get a hands-on demo of how to develop a machine learning model. The package allows users to pull data, implement machine learning algorithms to make predictions, and improve patient outcomes in real clinical settings. While there is other free software to develop predictive models, many organizations fail to provide a sufficient infrastructure to achieve value from it. Healthcare.ai provides tools to help walk a user through the process of data cleansing, model selection, performance evaluation, and interpretation. Finally, the package supports SQL-in, SQL-out data flow to be truly embedded into the analytics environment and make deployment reliable and consistent. The webinar will go through the capabilities of healthcare.ai and then go on to show the process of installation, real models being built, and deployed. We’ll dive into the R software environment and show you how easy it is to use machine learning in your analytics platform.

Levi Thatcher, Health Catalyst Director of Data Science and his team will provide a live demonstration of using healthcare.ai to implement a healthcare-specific machine learning model from data source to patient impact. Levi will go 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 since the announcement will be shared and answered during the session.

Wednesday, February 8
1:00-2:00 PM EST

During the webinar, we will:

  1. Describe and install healthcare.ai
  2. Build and evaluate a machine learning model
  3. Deploy interpretable predictions to SQL Server
  4. 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!

Dr. John Haughom, MD
Levi Thatcher, PhDDirector of Data Science, Health Catalyst

Levi did his graduate work at the University of Utah, focusing on atmospheric predictability. There he used ensemble methods to improve numerical models, in terms of both the lead time and estimated intensity of hurricane development. At Health Catalyst, Levi started out on the platform engineering team, creating software improvements to the company’s core ETL offering. Since he moved internally to lead the data science team, Levi founded healthcare.ai, the first open-source machine learning project focused on healthcare outcomes. He’s now working to integrate healthcare.ai into each of Health Catalyst’s products and make healthcare.ai the international center of collaboration for healthcare machine learning.

Dr. John Haughom, MD
Michael Mastanduno, PhDData Scientist, Health Catalyst

Mike Mastanduno joined Health Catalyst in November of 2016 as a Data Scientist. He received his PhD from Dartmouth College in Biomedical Engineering, where he designed hardware and software tools to aid in the early diagnosis of breast cancer. Mike’s dissertation culminated in a 60-patient clinical trial to evaluate the technology he had developed. Mike then went on to a postdoctoral fellowship at the Stanford School of Medicine where he won a National Institute of Health award to study medical imaging of ovarian cancers. During his academic career, Mike was able to publish over 10 journal papers, 25 conference papers, and won numerous awards for speaking and research. Since leaving Stanford, Mike studied with Insight Data Science and then joined Health Catalyst to continue improving healthcare through developing software tools. He is excited by the opportunity to impact patients nationwide by building machine learning and artificial intelligence models with Health Catalyst. You can see his contributions on healthcare.ai, find him teaching others how to think about machine learning problems, and implementing predictive analytics in clinical environments.