Dementia Risk

Dementia risk prediction research aims to identify individuals at high risk of developing dementia, enabling early intervention and improved patient care. Current efforts focus on leveraging machine learning, particularly employing advanced architectures like survival transformers, gradient boosting machines, and graph neural networks, to analyze diverse data sources including metabolomics, electronic health records, and clinical claims data. These models are being refined to improve accuracy, address missing data, and incorporate multiple risk factors, including genetic, lifestyle, and environmental influences, for more holistic and personalized risk assessments. The ultimate goal is to develop robust and reliable predictive tools for clinical use and to inform public health strategies for dementia prevention.