Description: Analyzing large quantities of raw complex data is often a challenge in the health care industry. During this webcast, there will be a demonstration of how unsupervised machine learning techniques can be applied to such data to help bring useful insights. This will include examples that use unsupervised learning techniques spanning dimensionality reduction, clustering and anomaly detection.

Description: "Through both informed policy debate and opinionated social media, there are many current event health topics actively discussed and debated. The question remains... how does one separate out subjective information, seek objective analysis, and at the same time consider where there might be more research needed to come to conclusions? This session will use several current event topicsas examples and discover insights as to where research has been performed, what it may or may not cover, and also note where lines are still blurred and may need further details to be studied. Topics that may be discussed include: How valuable and exact is the use of risk-adjustment for Medicare Advantage What is the health and mortality impact of receiving COVID-19 vaccinations"