Modeling Alzheimer's Disease Risk in Real World Data

Explore a conference talk on modeling Alzheimer's disease risk using real-world data presented at the Computational Genomics Summer Institute (CGSI) 2024. Delve into Timothy Chang's research, which integrates advanced computational methods with large-scale health data to better understand and predict Alzheimer's disease risk factors. Learn about the application of SNOMED CT embeddings in defining disease relationships, the importance of age-stratified longitudinal studies in dementia research, and the role of diverse biobanks like the UCLA ATLAS Community Health Initiative in precision health research. Gain insights into cutting-edge approaches for analyzing complex medical data and their potential impact on improving Alzheimer's disease risk assessment and management.