A Study on the Design of a Depression Diagnostic Framework Based on DepGPT and Neural Networks

This study explores the design of a framework for applying artificial intelligence and natural language processing techniques to the detection and treatment of depression. By combining neural networks and a large corpus and introducing a parameter-adjusted large-scale language model, DepGPT, the intelligent development of depression diagnosis and treatment model is realized. The framework design includes emotion understanding, cognitive process simulation, social interaction, personalization and adaptation, interpretability and trust, privacy and security, and compliance review, aiming to improve the accuracy and personalization level of depression diagnosis and treatment, and to ensure patient privacy and security. The results of this study promote the innovation and development of depression diagnosis and treatment technology and provide more effective treatment options for patients with depression.