Gain a high-level appreciation for artificial intelligence and how it can be applied to generate real-world evidence. Researcher Rashmee Shah will review prediction models and discuss machine learning models such as random forests, support vector machines and neural networks. Learners will examine newer approaches to generating real-world evidence, and the advantages, disadvantages, and limitations of each approach.

Learning Objective
Upon completion of this activity, participants should be able to:

• Review concepts of newer analytic methods for clinicians.
• Explain the advantages, disadvantages, and limitations of newer approaches.

Faculty
Rashmee U. Shah, MD MS

Acknowledgments
This activity is supported by Pfizer, Inc.

Date of Release: September 5, 2019
Term of Approval/Date of CME/CNE Expiration: September 5, 2020

Accreditation and Designation Statements
Physicians
The American College of Cardiology Foundation (ACCF) is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

The ACCF designates this online enduring materials for a maximum of 0.25 AMA PRA Category 1 Credits^TM. Physicians should only claim the credit commensurate with the extent of their participation in the activity.

While offering CME credits noted above, this program is not intended to provide extensive training or certification in the field.

Nurses
The American College of Cardiology Foundation (ACCF) is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center’s Commission on Accreditation.

The ACCF designates this educational activity for a maximum of 0.25 continuing nursing education contact hour(s).

While offering CNE credits noted above, this program is not intended to provide extensive training or certification in the field.