Research on Disease Diagnosis Method Combining Knowledge Graph and Deep Learning

Abstract

Focusing on the problem that existing disease diagnosis methods using deep learning rely heavily on labeled data in the auxiliary diagnosis process, and lack the experience and knowledge of doctors or experts, a disease diagnosis method combining medical knowledge graph and deep learning is proposed. The core of this method is a knowledge driven convolutional neural network (CNN) model. By using the entity linking and disam-biguation and knowledge graph embedding technologies to get the structured disease knowledge of medical knowledge graph, the word vector of disease features in the disease description text and the entity vector of corresponding knowledge are taken as multi-channel input of CNN, so as to represent different types of diseases from semantic and knowledge levels in the convolution process. Through training and testing on multiple disease description datasets, the experimental results show that the diagnostic performance of the proposed method is better than that of the single CNN model and other disease diagnosis methods, and verify that this method combining knowledge and data training is more suitable for the preliminary diagnosis of disease types of disease description.

Keywords

• knowledge graph embedding
• expert experience
• convolutional neural network (cnn)
• multi-channel
• disease diagnosis