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Levi Thatcher - Keynote Speaker
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Practical lessons in healthcare artificial intelligence


Artificial intelligence (AI) and machine learning are popular buzzwords, but what can they actually do improve the cost and quality of patient care? Drawing on lessons from his work in machine learning across US health systems, Levi will discuss lessons learned; the best place for quick efficiency gains; and how machine learning will eventually lead to optimized treatments, population health, and effective personal responsibility.

Technical Q&A with Levi Thatcher: A deeper dive into Artificial Intelligence and Machine Learning
  • What’s the difference between AI and machine learning
  • How can AI/ML improve healthcare in a practical way
  • What tools are needed to get started
  • How does one build a machine learning model
  • What data is needed for AI/ML
  • How to get clinicians excited about decision support



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Levi Thatcher
VP, 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, writing software for the company’s core ETL offering. After moving internally to lead the data science team, Levi founded, the first open-source machine learning project focused on healthcare outcomes.


He’s now working to integrate into each of Health Catalyst’s products and make the international center of collaboration for healthcare machine learning.

Levi Thatcher

HiNZ, PO Box 300125, Albany, Auckland 0752, New Zealand.

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