Patient-centered Deep Learning Model and Diagnosis Service for Persons with Alzheimer’s Disease Services
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 talk 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.
邱广华，博士，现任美国宾州州立大学教授，服务科学国际研究会(Services Science Global)的创始人和主席，国际电子工程学会（IEEE）智能运输系统学会服务与物流技术委员会的主席，运筹学和管理学研究协会（INFORMS）服务科学部的创建主席。他还担任INFORMS Service Science 和 International Journal of Services Operations and Informatics 的主编, IEEE Transaction on System, Man and Cybernetics 和 IEEE Transaction on Industrial Informatics的副主编,International Journal of Data Mining and Bioinformatics, Journal of Engineering and Applied Sciences的董事编辑，和多家国际期刊的客座编辑。
编辑 | 吴一锴