AI-ADRD: Accelerating interventions of AD/ADRD via Machine learning methods

University Of Pennsylvania, Philadelphia PA

Investigators

Linked publications & trials

Abstract

PROJECT SUMMARY Alzheimer's Disease and Alzheimer's Disease Related Dementias (AD/ADRD) are neurodegenerative diseases characterized by progressive loss of cognition and other neurobehavioral symptoms. In United States, there are approximately 5.8 million patients live with AD/ADRD, and 97% of these patients are older than 65. In the last decade, real-world data (RWD), including electronic health records (EHR) data and claims data, are becoming increasingly valuable for drug repurposing of AD. In this proposal, we plan to develop novel high dimensional semi-supervised learning and active learning methods, as well as associated high dimensional hypothesis testing procedures, to accelerate interventions of AD/ADRD, using drug repurposing of AD/ADRD as a motivating use case. We will address several key challenges in real-world data including the high dimensionality of the risk factors, concomitant medication use, and complex and heterogeneous progression trajectories of AD/ADRD. The success of this project will lead to novel machine learning and statistical learning methods for facilitating drug repurposing for AD/ADRD based on real-world data.