Joint Learning of Multiple Longitudinal Prediction Models by Exploring Internal Relations

Longitudinal prediction of the brain disorder such as Alzheimer’s disease (AD) is important for possible early detection and early intervention. Given the baseline imaging and clinical data, it will be interesting to predict the progress of disease for an individual subject, such as predicting the conversion of Mild Cognitive Impairment (MCI) to AD, in the future years. Most existing methods predicted different clinical scores using different models, or predicted multiple scores at different future time points separately. This often misses the chance of coordinated learning of multiple prediction models for jointly predicting multiple clinical scores at multiple future time points. In this paper, we propose a novel method for joint learning of multiple longitudinal prediction models for multiple clinical scores at multiple future time points. First, for each longitudinal prediction model, we explore three important relationships among training samples, features, and clinical scores, respectively, for enhancing its learning. Then, we further introduce additional relation among different longitudinal prediction models for allowing them to select a common set of features from the baseline imaging and clinical data, with

l

2,1

sparsity constraint, for their joint training. We evaluate the performance of our joint prediction models with the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, showing much better performance than the state-of-the-art methods in predicting multiple clinical scores at multiple future time points.