Modeling Disease Progression with Deep Neural Networks

Alzheimer's disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. In this paper, we consider the problem of predicting disease progression measured by the cognitive scores and selecting biomarkers predictive of the progression. Most previous work on predicting AD progression ignore the issue of missing data. Missing data poses a major difficulty for modeling longitudinal data since most statistical models assume feature-complete data. We proposed and applied a minimal recurrent neural network model with skip to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We have performed extensive experiments to demonstrate the effectiveness of the proposed model using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).