Deep Feature Learning for Disease Risk Assessment Based on Convolutional Neural Network With Intra-Layer Recurrent Connection by Using Hospital Big Data

This paper presents the analysis of real-life medical big data obtained from a hospital in central China from 2013 to 2015 for risk assessment of cerebral infarction disease. We propose a new recurrent convolutional neural network (RCNN)-based disease risk assessment multimodel by utilizing structured and unstructured text data from the hospital. In the proposed model, the convolutional layer becomes a bidirectional recurrent neural network by utilizing the intra-layer recurrent connection within the convolutional layer. Each neuron within convolutional layer receives feedforward and recurrent inputs from the previous unit and neighborhood, respectively. In addition to step-by-step recurrent operation, the region of context capture increases, thereby facilitating fine-grain feature extraction. Furthermore, we use a data parallelism approach over multimodel data during training and testing of the proposed model. Results show that the data parallelism approach leads to fast conversion speed. The RCNN-based model works differently from the traditional convolutional neural network and other typical methods. The proposed model exhibits a prediction accuracy of 96.02%, which is higher than those of typical existing methods.