Introducing the Machine Learning Marketplace

This new 2-hour breakout will address key questions stopping health systems from getting started

Healthcare is buzzing with talk about the potential benefits of machine learning and artificial intelligence. But key questions abound that stop health systems from getting started:

  • Can machine learning and AI only work in deep-funded projects with deep data scientific background, or can health systems get started with much more pragmatic approaches?
  • How do we get started if we want to begin somewhere with the greatest chances of having early success?
Introducing the Machine Learning Marketplace

This new 2-hour breakout is intended to address those questions. We have invited 12 stations providing a broad exposure to a wide variety of innovative uses for machine learning. Attendees will leave inspired to better understand use cases that could work in their system.

This session will be divided into two segments

The first hour segment will consist of presenters at 12 stations providing 5-minute overviews of how they are using machine learning.  The audience will be seated and able to see a broad array of use cases, and determine which of the use cases they would like to investigate further.

The second hour segment will consist of the audience walking around and conversing directly with the presenters at 12 individual stations.  Each of the health systems will be available to explain more in depth about their machine learning projects, including results and key lessons learned.

Look at the breadth of topics, speakers and use cases that will be covered.

A. Operationalizing Predictive Analytics within Critical Care Environments
Craig Rusin, Assistant Professor, Baylor College of Medicine
B. A Natural Language Processing Toolkit for Healthcare: Creating Happier Patients and Improving Healthcare Outcomes
Ali Lakhani, DM, Senior Big Data Scientist, Marketing Research & Strategic Intelligence, Sutter Health
C. Machine Learning in the Real-World: Improving Accuracy of Readmission Risk Reporting Enabling Service Line Reporting
Holly Burke, MHPA, Executive Director Clinical Innovation and Quality, Pulse Heart Institute
Needham Ward, MD, Chief Medical Officer, Pulse Heart Institute
D. No-Show Forecasting
Lixi Kong, MS, Senior Value Measurement Analyst, Dartmouth Hitchcock Medical Center
E. Congratulations, It’s a Model! Now What? (Technical Debt Involved in Sustaining a Production Prediction Model.)
Andrew Johnson, Manager of Data Science, Mission Health
F. Claims-based Machine Learning Applied to Opioid Use Disorder in Oregon: Features Identified, but Now What?
Maggie Bennington-Davis, MD, Chief Medical Officer, Health Share of Oregon
John Sanders, PhD, Chief Information Officer, Health Share of Oregon
G. Forecasting Inpatient Census for Operational Efficiency and Smarter Resource Allocation
Deborah Viola, MBA, PhD, Vice President, Data Management & Analytics, Westchester Medical Center
Noah Geberer, MBA, Senior Director Data Management & Analytics, Westchester Medical Center
H. Clinical No-Show
Dane Hudelson, Director Enterprise Data & Analytics, Sanford Health
I. Using Machine Learning to Detect Errors in Medical Data
Thomas Blanchard, Data Science Lead, Fresenius Medical Care North America
J. Risk Modeling for Falls in Value-Based Health Care Using NLP and Other Advanced Methods
Keegan Bailey, Strategy and Technology Leader, Acuitas Health
Dan Loman, Data Science Engineer, Acuitas Health
Francesca Romano, Data Science Engineer, Acuitas Health
K. Use the healthcare Data Operating System™ to Turn Data Analysts into Data Scientists
Justin Smith, PhD, Enterprise Director of Data and Analytics, Sanford Health

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