This product includes the webinar and SAM 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 SAM credit available: 1.0

How to claim credit: To earn SAM credit, all learners must watch the webinar, achieve a minimum score of 80% on the online quiz, and complete an online survey. To access the quiz, click "Submit credit" on the course homepage or click on "My Credit" on the menu to the left and then "submit credit" for the appropriate course.

This course is approved by the American Board of Pathology for 1.0 SAM credits.Physicians should only claim credit commensurate with the extent of their participation in the activity. Participants must successfully complete the online modular exams (answering at least 80% of the questions in a topic module correctly).

* 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 Board of Pathology
Self-Assessment Module: 1.00
Lennerz Presentation +SAM
AMP Regular Member: $10.00
AMP Technologist Member: $10.00
AMP Associate Member: $10.00
Non-member Price: $215.00