Hepatic encephalopathy prediction model and method based on bidirectional multi-modal attention network

The invention provides a hepatic encephalopathy prediction model and method based on a bidirectional multi-modal attention network. The hepatic encephalopathy prediction model comprises a data feature extraction module, an image feature extraction module, a bidirectional multi-modal attention module, a self-attention module and a prediction module. According to the hepatic encephalopathy prediction model and method based on the bidirectional multi-modal attention network, sample information can be comprehensively utilized, the detection effect can be improved, the convergence speed of the model can be effectively increased, the influence caused by noise can be reduced, the performance of the model can be improved, and the prediction accuracy of the hepatic encephalopathy can be improved. The defects that an existing hepatic encephalopathy prediction method is not high in prediction accuracy, and brain images and clinical data information cannot be comprehensively utilized are overcome. 本发明提供一种基于双向多模态注意力网络的肝性脑