Visual analysis of biomarkers selected via multi-task learning for modeling Alzheimer's disease progression

The prediction and modeling of chronic diseases such as Alzheimer's disease (AD) has received widespread attention in recent years. The field is tightly integrated with medical care, and recent advances in machine learning technology provide opportunities to train AD disease progression models. This trend has led to the exploration and design of new machine learning techniques for multimodal medical and health datasets to predict the occurrence and modeling process of AD. The purpose of this article is to perform a longitudinal and tracking analysis by machine learning models and explore core regions of the brain's components associated with AD progression that are important for brain degeneration. We summarized the biomarkers related with the progression of AD and checked them by neuropathology. On the basis of this, further generalization to the corresponding functional brain blocks was provided; the results showed that this is in line with the results in the field of clinical medicine and provides technical support for physician-assisted diagnosis.