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Recurrent convolutional neural network based multimodal disease risk prediction - 2019

Author(s) Name:  Yixue Hao,Mohd Usama,Jun Yang,M. Shamim Hossain and Ahmed Ghoneim
Journal name:  Future Generation Computer Systems
Conferrence name:  
Publisher name:  ELSEVIER
DOI:  10.1016/j.future.2018.09.031
Research Area:  Machine Learning
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 patients 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.

Volume Information:  Volume 92, March 2019, Pages 76-83
Journal Link:

https://www.sciencedirect.com/science/article/abs/pii/S0167739X18318843