Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11012474 | Future Generation Computer Systems | 2019 | 27 Pages |
Abstract
With the rapid growth of biomedical and healthcare data, machine learning methods are used in more and more work to predict disease risk. However, most works use single-mode data to predict disease risk and only few works use multimodal data to predict disease risk. Thus, a new multimodal data-based recurrent convolutional neural network (MD-RCNN) for disease risk prediction is proposed. This model not only can use patient's structured data and text data, but also can extract structured and unstructured features in fine-grained. Furthermore, in order to obtain the highly non-linear relationships between structured data and unstructured data, we use deep belief network (DBN)to fuse the features. Finally, we experiment with the medical big data of a Chinese two grade hospital during 2013-2015. Experimental results show that the accuracy of MD-RCNN algorithm can reaches 96% and outperforms several state-of-the-art methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Yixue Hao, Mohd Usama, Jun Yang, M. Shamim Hossain, Ahmed Ghoneim,