Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease

New research from our group describes a computational model that can be used for early detection of individuals at risk for clinical decline. This machine-learning model is capable of combining longitudinal, multimodal data (MRI, cognitive, genetic and demographic data) to predict the symptom progression patterns at the single subject level.

With the prevalence of Alzheimer’s disease rapidly increasing, the identification of the declining individuals a priori would provide a critical window for early intervention and preventative treatment planning. While this work holds crucial clinical utility for Alzheimer’s disease, this model can be applicable to other neurodegenerative diseases .

For more information about this research, click the link to read the paper Bhagwat et al. (2018)