A Transformer-Based Diagnostic Prediction Model for Bipolar Disorder

In today's world, the diagnosis of bipolar disorder mainly involves a comprehensive assessment of the patient's symptoms, course of illness, family history and other factors, which has certain subjective limitations. In this study, by analyzing the existing research based on the Transformer prediction model, the Transformer bipolar disorder diagnosis prediction model was established through the two aspects of Encoder and Decoder, and the RMSprop algorithm was used to set the loss function to train the model, and finally the accuracy and stability of the model were verified by the real case data of some hospitals. It is helpful for us to diagnose and treat bipolar disorder in real life.