Neural Network Models for Personalized Mental Health Interventions in Psychiatry

Abstract

A requirement exists for future predictive models which need to adjust their mental health intervention approach for each patient's individual characteristics in psychiatric fields. Clinical and behavioral mental health outcome analysis implements Transformer-based Neural Networks together with the Bidirectional Encoder Representations from Transformers (BERT) model. Single-input BERT excels at healthcare documentation analysis with patient stories and psychological testing by making use of its contextual processing elements to reach better treatment suggestions. The TensorFlow framework serves as the implementation base because of its deep learning platform which enables training and deployment of the model with peak performance. The model demonstrates its successful experimental identification of faint psychological patterns to help produce better diagnostic results alongside individual therapy design. The proposed method generates significant effects on digital psychiatry because it supplies scalable and intelligent solutions for mental health care delivery