The growth and development of open-source software and digitized and publicly available data sources have made the application of machine learning (ML) methods to laboratory medicine highly accessible. A recent IFCC working group has published guidelines for the use of machine learning in laboratory medicine. In this second of two webinars, members of the working group will present practical illustrations machine learning approaches using a case-based dialogue between two presenters. Specific strengths and weaknesses of varied strategies, representing best practices vs. common pitfalls, will be highlighted. All laboratory professionals will benefit from a basic understanding of ML concepts, as these technologies increasingly become part of our workflows. As with other emerging technologies in our laboratories, some of us will seek a more advanced understanding to contribute to the development and technical evaluation of these methods, engaging in driving this new area forward as collaborators in method development and implementation. More of us will be consumers and decisions makers, called upon to critically review and generally evaluate ML-based products to ensure their safe, effective, and sustained use. Broad recognition of common failures and accepted best practices by our community will improve the quality of science and set higher standards for ML innovations being applied in clinical laboratories.

This webinar comprises of two following presentations of 20 min each followed by 20 min of panel discussion at the end.

Moderator: Dr. Stephen Master

Talk 1- "Case Study and Analysis, Part I" -  Dr. Stephen Master

Talk 2- "Case Study and Analysis, Part II" - Dr. Shannon Haymond

Please feel free to share this event in your social media with #IFCCLive Webinars.

Click here to convert to your time zone