Predicting Infectious Disease Using Deep Learning and Big Data.

ABSTRACT: Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrat

SUBMITTER: Chae S 

PROVIDER: S-EPMC6121625 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature