A Novel Disease Diagnosis Method Using Combining Knowledge Graph and Deep Learning

Abstract

Disease diagnosis methods based on deep learning have some shortcomings in the auxiliary diagnosis process, such as relying heavily on labeled data and lack of doctor or expert experience knowledge. Based on the above background, this study proposes a disease diagnosis method combining medical knowledge atlas and deep learning (CKGDL). The core of the method is a knowledge-driven convolutional neural network (CNN) model. The structured disease knowledge in the medical knowledge map is obtained through entity link disambiguation and knowledge map embedding and extraction. The disease feature word vector and the corresponding knowledge entity vector in the disease description text are used as the multi-channel input of CNN, and different types of diseases are expressed from the semantic and knowledge levels in the convolution process. Through training and testing on multiple types of disease description text data sets, the experimental results show that the diagnostic performance of this method is better than that of a single CNN model and other disease diagnosis methods. And further verified that this method of joint training of knowledge and data is more suitable for the initial diagnosis of the disease.