Recurrent convolutional neural network based multimodal disease risk prediction

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.