Integrating Multimodal Deep Learning With CBT-ED

Abstract

This chapter merges traditional diagnostic frameworks with state-of-the-art technological interventions, focusing on the integration of machine learning and the internet of things (IoT) to understand and address eating disorders. It provides an overview of diagnostic frameworks, including emerging disorders like binge eating disorder (BED) and avoidant/restrictive food intake disorder (ARFID), alongside sociodemographic trends and treatment approaches, particularly cognitive-behavioral therapy for eating disorders (CBT-ED) Additionally, the chapter introduces a novel multimodal deep learning model, combining RoBERTa natural language processing and MaxViT image classification, to identify social media content promoting eating disorders. The model's deployment in a time-series analysis of Twitter hashtags reveals nuanced prevalence trends. Overall, this research bridges conventional methodologies with advanced machine learning, offering insights into the complex landscape of eating disorders.