The invention discloses an auxiliary diagnosis
system for large-scale medical records based on
deep learning. The auxiliary diagnosis
system comprises a cyclic neural
network model, a
convolutional neural network model and a fusion calculating unit. The cyclic neural
network model is obtained through training the cyclic neural network based on large-scale previous medical records with a diagnosisconclusion. The
convolutional neural network model is obtained through training the
convolutional neural network through the artificially generated medical records, wherein the artificially generatedmedical records are based on a correspondence between diseases and symptoms in a
knowledge graph and obtained through arrangement combination of
disease incidence rate and symptom incidence rate. Information of gender, age, main complaint, existing
disease history, previous history,
personal history, family history, physique examination and auxiliary examination is input into the cyclic neural
network model and the convolutional neural network model. The fusion calculating unit generates diagnosis prompt which is related with the
medical record according to biological calculating results of the cyclic neural network model and the convolutional neural network model.