Enhance Prediction of Alzheimer’s Disease with Generative AI: Missing Data and Fairness

Chenxi Yuan, PhD
Perelman School of Medicine
University of Pennsylvania

Abstract: Predicting progression from normal cognition to Alzheimer's disease (AD) using longitudinal data holds great promise for the early identification of high-risk patients. However, such longitudinal studies suffer from small sample sizes and sparse availability of some data elements. This problem is further compounded by missingness. Missing data poses multiple challenges for longitudinal studies of AD, such as reducing the sample size, increasing selection bias, and reducing statistical power. This is particularly problematic for populations under-represented in the data including individuals affected by AD and individuals from racial and ethnic minority groups. In this talk, I will introduce a novel generative AI model to impute missing neuroimaging data in longitudinal studies of AD. The model focuses on generating missing images at a designated single visit by conditioning one or more observed images from other time points. In addition to missingness, populations under-represented in the data, such as individuals from racial and ethnic minorities, pose challenges for longitudinal studies of AD, such as reducing the sample size, increasing selection bias, and reducing statistical power. Consequently, I will introduce a prognostic study that investigated the algorithmic fairness of machine learning models for predicting the progression of AD and discuss the opportunity of building generative AI models to augment data for under-represented groups to enhance fairness.

Biography: Chenxi (Chelsea) Yuan is a postdoctoral researcher at the University of Pennsylvania Perelman School of Medicine in the Department of Biostatistics, Epidemiology, and Informatics. She earned her M.S. and Ph.D. in Industrial Engineering from the University of Florida in 2017 and Northeastern University in 2022. Dr. Yuan’s research interests are AI for social goods. At present, her work focuses on the development of generative AI that facilitates precision medicine and equitably improves health outcomes. She received multiple awards, including the Outstanding Student Scholarship from Northwest University in 2015, the Achievement Award Scholarship from the University of Florida in 2016, the MIE Graduate Student Conference Award from Northeastern University in 2020, and the Reviewer’s Favorite Paper Award in the ICED Conference in 2023.