Research On TCM Diagnosis Method Based On Multi-task Learning

With the rapid development of artificial intelligence,artificial intelligence has become a relatively hot research direction at home and abroad,and Chinese medicine has gradually moved towards internationalization.Looking at the current situation of AI intelligent medical treatment at home and abroad,we can see that the current AI focuses on medical image processing.And in the field of disease diagnosis,research on the smart diagnosis and treatment of more common conventional drugs is still lacking.This article focuses on the algorithm research and improvement of TCM syndromes,diseases and prescription generation,and promotes TCM diagnosis more intelligent.In the research on the diagnosis methods of syndromes and diseases,this paper organizes the real electronic medical records and Chinese medicine records.The pre-training of the model uses the Albert model,and the model is pre-trained with Chinese medicine knowledge to improve the performance of the model.And perform migration learning and multi-task processing,and multi-task output through the shared layer to obtain syndromes and diseases.The research model at this stage is divided into encoding and decoding stages.In the encoding stage,the syndrome attention mechanism,symptom attention mechanism,and medical history attention mechanism are added.These three attention mechanisms are fused into one multi-head attention according to their own attention weights.Force mechanism,the long and short-term memory network(LSTM)has been improved by adding a multi-head attention mechanism.In this stage,comparative experiments were carried out.Under the same data and the same experimental environment,comparative experiments and quantitative analysis of prediction accuracy were carried out.Compared with other models,the model loss function proposed in this topic has better training effects and higher prediction accuracy.In the research method of prescription generation,this topic studies the relationship between the disease and the induced cause,the relationship between the disease and the cause,the relationship between the disease and the drug,and an effective prescription is generated to provide to the patient according to the characteristics of Chinese medicine.According to the characteristics of traditional Chinese medicine,different attentions are designed in the long and short-term memory network of encoding and decoding to complete the generation model of the prescription,and the model is improved.The prescription generation model is designed as follows.The patient’s symptoms are first sent to the long and short-term memory network(LSTM)in the coding stage one by one,and attention is added to the main and secondary syndromes,and the attention of traditional Chinese medicine to the disease is added to the decoding part,and the required prescription is finally generated.This subject also carried out a comparative experiment,and conducted training in different models under the same data and the same experimental environment.Because of the particularity of Chinese medicine prescriptions,the experimental results were specially evaluated by Chinese medicine experts from two aspects: 1.The effectiveness of Chinese medicine 2.Chinese medicine compatibility.After conducting comparative experiments,it is found that the model designed in this topic can generate traditional Chinese medicine prescriptions more effectively,especially the compatibility of traditional Chinese medicines,which has been significantly improved.The realization of this topic has achieved the construction of two models.The proposed algorithm can more effectively generate syndromes,diseases,and Chinese medicine prescriptions,and serve as a pavement for the realization of an expert system in the future.It can also provide data support and provide effective evidence for it.Symptoms,diseases,and methods of formulating prescriptions.