Track: Information Technology and Information Systems

Abstract

Because pharmaceutical companies have failed to develop Alzheimer’s disease (AD) cure and treatment as of today, AD early detection and intervention becomes increasingly clear to be the best choice of improving quality of life for persons with AD at least in the near future. Thus, developing patient-centric predictive models and enabling self-diagnosis services are of great potential. This paper presents how recurrent neuron neatwork (RNN) models can be adopted in the AD early diagnosis modeling (AD-EDM). In particular, we show that the improved prediction accuracy of RNN AD-EDM can contribute to the delivery of self-diagnosis services for preclinical/early AD patients. By leveraging the fast development of big data technologies and machine learning methods, our AD-EDM tools will make a difference in discovering non-pharmacologic therapy solutions to slow AD progression.