Abstract：Aiming at the accurate and rapid diagnosis of complications, this paper proposes an auxiliary diagnosis model based on knowledge graph, representation model and deep neural network. Firstly, a medical knowledge graph is constructed, which is represented by the vector for each entity and relation. Then according to chief complaints of the patients, the symptom entities are detected and again represented by vectors. Eventually, the above two kind of vectors are input to the CNN-DNN classification model joint with the index representation to diagnose the complications. The experiment chooses three complications of diabetes: hypertension, diabetic nephropathy and diabetic retinopathy. The accuracy of the proposed model is improved by 5%, 5%, 14% compared with the classical machine learning methods, respectively; and 27%, 6%, 9% higher than that of previous DNN model.
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2020, Vol. 34 
