Knowledge-enhanced Pre-training large language model for depression diagnosis and treatment

Depression, a pervasive psychiatric disorder characterized by concealment, dependence on expert judgment, and a notable rate of misdiagnosis, poses a substantial burden on society. To enhance the diagnosis and treatment of depression, this study puts forth a proposition of employing knowledge-enhanced pre-training technology leveraging large language models. By integrating domain knowledge and depression knowledge graph directives, the pre-trained model undergoes optimization. Expert involvement in depression diagnosis and treatment fosters a guided learning process facilitated by expert feedback. Through the application of dialogue therapy, the efficacy of treatment is augmented. This technical approach aims to ameliorate the societal burden by improving the diagnosis and treatment of depressed individuals.