Domain-knowledge driven cognitive degradation modeling for Alzheimer's disease

Copyright SIAM. Cognitive monitoring and screening holds great promises for early detection and intervention of Alzheimer's disease (AD). A critical enabler is the personalized degradation model to predict the cognitive status over time. However, estimating such a model using individual's data faces challenges due to the sparsity and fragmented nature of the cognitive data of each individual. To mitigate this problem, we propose novel methods, called the collaborative degradation model (CDM) together with its extended network regularized version, the NCDM, which can incorporate useful domain knowledge into the degradation modeling. While NCDM results in a difficult optimization problem, we are inspired by existing non-negative matrix factorization methods and develop an efficient algorithm to solve this problem and further provide theoretical results that ensure that the proposed algorithm can guarantee non-increasing property. Both simulation studies and the real-world application to AD are conducted across different degradation models and sampling schemes, which demonstrate the superiority of the proposed methods over existing methods.

Domain-knowledge driven cognitive degradation modeling for Alzheimer's disease Conference Paper