A Transformer-CNN Hybrid Model for Cognitive Behavioral Therapy in Psychological Assessment and Intervention for Enhanced Diagnostic Accuracy and Treatment Efficiency

The use of Cognitive Behavioral Therapy (CBT) as a method for psychological assessment and intervention has shown to be quite successful. However, by utilizing advancements in artificial intelligence and natural language processing techniques, the diagnostic precision and therapeutic efficacy of CBT can be significantly improved. For CBT in psychological evaluation and intervention, we suggest a unique Transformer CNN hybrid model in this work. The hybrid model combines the strengths of the Transformer and Convolutional Neural Network (CNN) architectures. While the CNN model successfully extracts local and global features from the input sequences, the Transformer model accurately captures the contextual dependencies and semantic linkages in the text data. It intends to enhance the model's comprehension and interpretation of the complex linguistic patterns involved in psychological evaluation and intervention by merging these two algorithms. On a sizable collection of clinical text data, which includes patient narratives, treatment transcripts, and diagnostic reports, we undertake comprehensive experiments. The proposed Trans-CNN hybrid model outperformed all other methods with an impressive accuracy of 97%. In diagnosing psychiatric problems, the model shows improved diagnosis accuracy and offers more effective therapy advice. Our hybrid model's automatic real-time monitoring and feedback capabilities also make it possible for prompt intervention and customized care during therapy sessions. By giving doctors a formidable tool for precise evaluation and efficient intervention, the suggested approach has the potential to revolutionize the field of CBT and enhance patient outcomes for mental health. In order to improve the diagnostic precision and therapeutic efficacy of CBT in psychological evaluation and intervention, this work provides a transformational strategy that combines the advantages of the Transformer and CNN architectures.