Speaker(s):
Yasser Elmanzalwai, PhD, Faculty Member, Geisinger - has nothing to disclose.
Moderator(s):
Bruce Levy, MD, Staff, Geisinger - has nothing to disclose.
Learning Objectives:
At the conclusion of this session, the participant should be able to:
- Describe the framework for applied machine learning in healthcare
- Differentiate between black box and white box models
- Evaluate the Trade-Offs between interoperability and performance
- Identify the potential benefits and drawbacks of AI technology in healthcare and explore ways in which AI could be used to improve patient outcomes, enhance clinical decision-making, and streamline healthcare delivery
- Describe how to build and use XAI predictive models
- Discuss the promise and challenges of integrating XAI approaches for predictive analyses of electronic health record (EHR) data
Disclosure of Relevant Financial Relationships with/without Commercial Interests:
The Planning Committee consisting of Bruce Levy, MD, Activity Director, Amanda Staskiel, RN, Dean Parry, RPh and My-Trang Dang, DO, PhD have no identified disclosures.
CE Committee Member/Content Reviewers have nothing to disclose.
Any/All relevant financial relationships have been mitigated.
Content Disclosure:
This presentation/content is HIPPA compliant.
Commercial Support for this Session
None.
Session date:
11/26/2024 - 12:00pm to 1:00pm EST
Location:
Geisinger Medical Center
100 North Academy Ave.
Danville, PA
17821
United States
See map: Google Maps
Add to calendar:
- 1.00 ACPE
- 1.00 AMA PRA Category 1 Credit™
- 1.00 ANCC
- 1.00 Participation Credit