A Survey of Disease Progression Modeling Techniques for Alzheimers Diseases Modeling and predicting progression of chronic diseases like Alzheimeru0027s 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 multimodality 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: multitask model, time series model and deep learning. In particular, we discuss the basic structural elements of most representative multitask learning algorithms, and analyze a multitask disease prediction model based on longitudinal time. Lastly, some potential future research direction is given.

Deep and Joint Learning of Longitudinal Data for Alzheimers Disease Prediction Abstract   Alzheimeru0027s disease (AD) is an irreversible and progressive neurodegenerative disease. The close AD monitoring of this disease is essential for the patient treatment plan adjustment. For AD monitoring, clinical score prediction via neuroimaging data is highly desirable since it is able to reveal the disease status, adequately. For this task, most previous studies are focused on a single time point without considering relationship between neuroimaging data (e.g., Magnetic Resonance Imaging (MRI)) and clinical scores at multiple time points. Differing from these studies, we propose to build a framework based on longitudinal multiple time points data to predict clinical scores. Specifically, the proposed framework consists of three parts, feature selection based on correntropy regularized joint learning, feature encoding based on deep polynomial network, and ensemble learning for regression via the support vector regression method. Two scenarios are designed for scores prediction. Namely, scenario 1 uses the baseline data to achieve the longitudinal scores prediction, while scenario 2 utilizes all the previous time points data to obtain the predicted scores at the next time point, which can improve the score predictionu0027s accuracy. Meanwhile, the missing clinical scores at longitudinal multiple time points are imputated to solve the incompleteness of the data. Extensive experiments on the public database of Alzheimeru0027s Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed framework can effectively reveal the relationship between clinical score and MRI data and outperforms the stateoftheart methods in scores prediction.