This product includes the webinar and CME/CMLE credit


Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex output and the decision to include a variant in the final report remains challenging.  Machine learning is one approach to mine big data and derive models for decision-making.  Given that bioinformatics pipelines generate mostly structured, discrete data, the setting is ideal to assess a machine learning decision support system.  A decision support tool for variant reporting is a relevant approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.  How can this be accomplished?  What are other use cases? What are the concrete steps for implementation?  In this upcoming webcast, Dr. Joe Lennerz from the Center for Integrated Diagnostics, Massachusetts General Hospital/Harvard Medical School will address these questions.   

Speaker: Jochen K. Lennerz, MD, PhD

Moderator: Sabah Kadri, PhD

Duration: 1 hr

Level of Instruction: Intermediate

Last day to purchase course and claim credit: March 20, 2020

Maximum CME/CMLE credit available: 1.0

How to claim credit: To earn CME/CMLE credit, all learners must watch the webinar and then complete the online survey. To access the online survey, click "Submit credit" on the course homepage, or click on "My Credit" on the menu to the left.

AMA PRA Category 1 Credit(s)™

This activity has been planned and implemented in accordance with the Essential Areas and policies of the Accreditation Council for Continuing Medical Education through the joint providership of American Society for Clinical Pathology (ASCP) and Association for Molecular Pathology (AMP). The American Society for Clinical Pathology (ASCP) is accredited by the ACCME to provide continuing medical education for physicians.

The ASCP designates this enduring material for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should claim only credit commensurate with the extent of their participation in the activity.

This continuing medical laboratory education activity is recognized by the American Society for Clinical Pathology for 1.0 hours of CMLE credit. ASCP CMLE credit hours are acceptable for the ASCP Board of Certification (BOC) Certification Maintenance Program (CMP). CMLE credit hours meet the continuing education requirements for the ASCP Board of Certification Credential Maintenance Program (CMP) and state relicensure requirements for laboratory personnel. Participants should claim only the credit commensurate with the extent of their participation in the activity. 

* You may not submit SAMs and CME/CMLE credit for the same content.

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Course Information
Course Date:
September 10, 2019
Course Objectives
  • Recognize the potential of machine learning to facilitate interpretation of genomic data
  • Distinguish 6 necessary components to address a problem using machine learning
  • Compare the relevance of data models vs. flexibility
Using Machine Learning to Improve Variant Reporting
Individual topic purchase: Selected
American Medical Association
Continuing Medical Education: 1.00
American Society for Clinical Pathology
CMLE: 1.00
Lennerz Presentation +CME/CMLE
AMP Regular Member: $5.00
AMP Technologist Member: $5.00
AMP Associate Member: $5.00
Non-member Price: $205.00