A Survey of Disease Progression Modeling Techniques for Alzheimer's Diseases

Abstract

Modeling and predicting progression of chronic diseases like Alzheimer's disease (AD) has recently received much attention. Traditional approaches in this field mostly rely on harnessing statistical methods into processing medical data like genes, MRI images, demographics, etc. Latest advances of machine learning techniques grant another chance of training disease progression models for AD. This trend leads on exploring and designing new machine learning techniques towards multi-modality medical and health dataset for predicting occurrences and modeling progression of AD. This paper aims at giving a systemic survey on summarizing and comparing several mainstream techniques for AD progression modeling, and discuss the potential and limitations of these techniques in practical applications. We summarize three key techniques for modeling AD progression: multi-task model, time series model and deep learning. In particular, we discuss the basic structural elements of most representative multi-task learning algorithms, and analyze a multi-task disease prediction model based on longitudinal time. Lastly, some potential future research direction is given.