A Hybrid System Based on Bayesian Networks and Deep Learning for Explainable Mental Health Diagnosis

Mental illnesses are becoming one of the most common health concerns among the population. Despite the proven efficacy of psychological treatments, mental illnesses are largely underdiagnosed, particularly in developing countries. A key factor contributing to this is the scarcity of mental health providers capable of diagnosing. In this work, we propose a novel method that combines the general capabilities and accuracy of Large Language models with the explainability of Bayesian Networks. Our system analyzes descriptions of symptoms provided by users and written in natural language and, based on these descriptions, asks questions to confirm or refine the initial diagnosis made by the deep learning model. We trained our model on a large-scale dataset collected from various internet sources, comprising over 2.3 million data points. The initial prediction from the Large Language model is refined through symptom confirmation questions derived from a probabilistic graphical model constructed by experts based on the DSM-5 diagnostic manual. We present results from symptom descriptions sourced from the internet and clinical vignettes extracted from behavioral science exams, demonstrating the effectiveness of our hybrid model in classifying mental health disorders. Our model achieves high accuracy in classifying a wide range of mental health disorders, providing transparent and explainable predictions.