This event is part of the Fall 2022 Interdisciplinary Seminar Series on Statistical Methodology for Social and Behavioral Research.
Machine Learning Prediction Modeling for Longitudinal Outcomes in Older Adults
Presented by Dr. Jaime Lynn Speiser, Wake Forest University
Friday, December 10
12 p.m. ET
Prediction models aim to help medical providers, individuals and caretakers make informed, data-driven decisions about risk of developing poor health outcomes, such as fall injury or mobility limitation in older adults. Most models for outcomes in older adults use cross-sectional data, although leveraging repeated measurements of predictors and outcomes over time may result in higher prediction accuracy. This seminar talk will focus on longitudinal risk prediction models for mobility limitation in older adults using the Health, Aging, and Body Composition dataset with a novel machine learning method called Binary Mixed Model (BiMM) forest. I will give an overview of two common machine learning methods, decision tree and random forest, before introducing the BiMM forest method. I will then apply the BiMM forest method for developing prediction models for mobility limitation in older adults.
Dr. Jaime Lynn Speiser is an Assistant Professor in the Department of Biostatistics and Data Science at Wake Forest School of Medicine. She pioneered a novel machine learning methodology framework for developing prediction models for clustered and longitudinal binary outcomes called Binary Mixed Model (BiMM) forest. Dr. Speiser is an expert in developing prediction models using machine learning for applications in medicine. Her recent focus is developing prediction models for outcomes in older adults such as falls and mobility limitation using a mix of social, behavioral, cognitive, and psychological comorbidity variables.