Speaker: Zoe Kourtzi, University of Cambridge
Title: Machine Learning for Predictive Prognostic Trajectories in Dementia
Alzheimer’s disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning that mines multimodal data from MCI patients to derive individualised prognostic scores of cognitive decline due to AD. Our approach affords the generation of a predictive and interpretable marker of individual variability in progression to dementia due to AD based on cognitive data alone. Including non-invasively measured biological data (grey matter density, APOE 4) enhances predictive power and clinical relevance. Our trajectory modelling approach has strong potential to facilitate effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification with important implications for clinical practice and discovery of personalised interventions.