## 4 Discussion  
In this work, we have developed a model that combines the scalable, powerful predictive capabilities and adaptability of large language models with the transparency and clinical soundness of Bayesian networks. This architecture aims to create an accurate diagnostic tool for mental conditions, characterized by its explainability and adherence to the clinical standards of psychiatric diagnosis. To accomplish this, we constructed an extensive pretraining and training dataset, amassing over 2.4 million data points from various forums and online sources. These data were carefully processed and anonymized to facilitate the training of a large language model using modern training methods. In addition, we designed a Bayesian network comprising a database of 42 conditions and 114 symptoms. This network enhances the predictive accuracy of the model through a question-and-answer process that echoes the differential diagnosis approach used by healthcare professionals. The integration of this Bayesian network considerably enhances the initial performance of the predictions of the deep learning model even by 20 percentage points in accuracy, underlining the effectiveness of the proposed hybrid approach. We think that the integration of deep learning models, with their capability for learning high-quality mappings from raw text to prediction probabilities, and probabilistic models, useful at distilling clinical knowledge accumulated over years of professional practice, represents a promising and fruitful area of research. There is plenty of room for improvement and future development for our work. First, we have utilized a probabilistic network that, although it reflects the relationships between conditions and symptoms as delineated in diagnostic manuals, still poses significant potential for improvement. Contemporary psychopathological research is increasingly focusing on conceptualizing mental conditions as states of intricate networks comprising symptoms, biological factors, and environmental and social influences. Integrating these complex networks into our model could substantially boost its predictive power and help professionals understand the multifaceted development of mental health disorders. Another growing research area involves causal analysis based on Bayesian network models. Tools like do-calculus allow causal analysis to delve into far more complex inquiries than the correlational analysis offered by standard Bayesian networks. This is particularly pertinent in mental health, where symptoms often have multiple causes. For instance, predicting a condition as probable given a symptom differs fundamentally from determining that a symptom would not exist without the condition being present. The latter is a causal question that traditional probabilistic models struggle to address effectively. An illustrative case is a depressed mood, which can be due to various causes, including grief. The critical question here is whether the symptom is the result of a mental health condition. By expanding our model to incorporate causal analysis tools, we could significantly improve the capacity of the model to provide insightful and relevant responses. Additionally, the design of our network that is based on the prior probabilities of conditions obtained from an initial prediction based on the input from the user, opens up opportunities for adapting to the large variability encountered in different demographics and users. Specifically, we can adjust the network to account for the unique characteristics of each user by modifying the prior probability of conditions with a user-specific factor based on specific features of each person. This adaptability is crucial in psychology, where the prevalence of mental health disorders and symptoms varies significantly based on factors such as ethnicity, age, and gender. Although our study has demonstrated the potential of our hybrid method for the classification of mental health conditions, there are some limitations that should be considered in future research.