Nancy Glenn Griesinger Presented: Artificial Intelligence in Cancer Prognosis and Prediction: The Role of a Statistician
“Artificial intelligence, it’s for real!” That statement was on a poster in my department over 40 years ago. While there have been constant advancements in artificial intelligence (AI) over the years, recent research highlights a remarkable resurgence of AI in several areas. One such area is machine learning, a sub-discipline of AI that focuses on computing systems as well as how such systems automatically improve
through experience. Machine learning applications are in practically every area, from business to biology. This research focuses on a computational biology application – the statistician’s role in machine learning applications in cancer prognosis and prediction.
This role is typically to build predictive models, including parametric maximum likelihood models. We instead employ nonparametric methodologies, such as empirical likelihood, in lieu of parametric methodologies in cancer prognosis and prediction. An advantage of nonparametric approaches is that they are distribution-free methodologies that relax distributional assumptions, such as normality, while maintaining accurate predictions.
- Monday, March 27th, 2023, Oral In-Person Presentation, “Artificial Intelligence in Cancer Prognosis and Prediction: The Role of a Statistician.”
- Thursday, March 30th, 2023, Online Workshop, “Non-Statistician’s Guide to Using R with the Correct Statistical Procedures.”
https://www.tsu.edu/about/administration/division-of-academic-affairs-and-research/research/
https://mobilemathlab.com/wp-content/uploads/2023/05/Research-and-Innovation-Week-program-1.pdf