Methods: Mind the Gap

Webinar Series

A Primer in Machine Learning in Epidemiology and Health Outcomes Research

August 31, 2022, 12:00 p.m. ET
Timothy Wiemken
Timothy Wiemken, Ph.D., M.P.H.

Pfizer

*A recording of this webinar will be available approximately two weeks after the session.

About the Webinar

Machine learning has become a hot topic in many areas of research and may have utility for answering many novel questions in epidemiology. The purpose of this webinar is to provide an overview of the salient concepts surrounding supervised machine learning methods and their application to epidemiologic problems. Concepts covered include comparisons of machine learning and ‘traditional’ statistical methodologies, feature engineering, training, hyperparameter tuning, testing a predictive model, and unpacking the black box of predictive models using explainers. Attention will be given to common machine learning methods such as random forests, neural networks, and causal methods with discussion of the bias-variance tradeoff. Automated methodologies (autoML) will also be covered with discussion of the importance of feature engineering over model selection.

About Timothy Wiemken

Dr. Timothy Wiemken is the Senior Director of clinical epidemiology for the Pfizer mRNA vaccine platform. His work focuses on data-driven approaches to solve pressing clinical public health issues. Prior to joining Pfizer, he was Associate Professor of Medicine in the Saint Louis University School of Medicine, Department of Internal Medicine, Division of Infectious Diseases, and the Director of Infectious Diseases Epidemiology at SSM Health, Saint Louis University Hospital. He has over 200 peer-reviewed publications, book chapters, and scientific reports in the area of infectious disease prevention. He enjoys spending time with his family, watching horror movies, and writing music.


This webinar will be captioned in real time. Individuals needing reasonable accommodations should contact MindtheGap@od.nih.gov. Requests should be made at least 5 business days before the event.

Last updated on July 12, 2022

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