The application provides a model training method and a bipolar disorder
personalized treatment dialogue generation method and device, belonging to the technical field of
deep learning, aiming at the problems that the current diagnosis accuracy of
bipolar mood disorder is not high and the matching of treatment dialogue with patients is poor, the model training can be performed on a multi-
modal language model according to a voice
signal with significant characteristics of
bipolar mood disorder, and a target classification model for
bipolar mood disorder classification is obtained. Since the voice
signal contains characteristics such as
semantics, tone, pronunciation
rhythm and other characteristics directly related to bipolar
mood disorder expressed by the patient, the target classification model can be used for accurate classification of bipolar
mood disorder, and when the target classification model is used for classification of bipolar
mood disorder, the excessive dependence on the subjective diagnosis of doctors can be avoided, and the accuracy of the classification prediction of bipolar mood disorder is effectively improved.