Predictive Modeling of the Progression of Alzheimer's Disease with Recurrent Neural Networks.

ABSTRACT: The number of service visits of Alzheimer's disease (AD) patients is different from each other and their visit time intervals are non-uniform. Although the literature has revealed many approaches in disease progression modeling, they fail to leverage these time-relevant part of patients' medical records in predicting disease's future status. This paper investigates how to predict the AD progression for a patient's next medical visit through leveraging heterogeneous medical data. Data provided by the National Alzheimer's Coordinating Center includes 5432 patients with probable AD from August 31, 2005 to May 25, 2017. Long short-term memory recurrent neural networks (RNN) are adopted. The approach relies on an enhanced "many-to-one" RNN architecture to support the shift of time steps. Hence,

SUBMITTER: Wang T 

PROVIDER: S-EPMC6003986 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature