## 2 Materials and Methods
### Building the Database  
#### DSM-V Overview  
Traditionally, mental health diagnoses are performed using diagnostic manuals, which provide criteria for identifying specific conditions. The most widely recognized of these manuals are the DSM-5 and ICD-11 [46; 63]. These guides play a crucial role in standardizing the diagnosis of mental health disorders, offering a structured approach to understanding the symptomatology necessary to classify a condition.  
To illustrate, according to DSM-5, the two most important core symptoms of major depressive disorder are (1) a depressed mood most of the day and/or (2) markedly diminished interest or pleasure. At least three or four more of the other seven symptoms must be met to be diagnosed with a depressive disorder, which means that depressive disorders themselves do not have consistent symptoms and vary greatly between individuals.  
Doctors use standardized questionnaires to confirm symptoms. For example, the PHQ-9 (Patient Health Questionnaire-9) is an assessment of the nine symptoms criteria in the DSM-5. It should be noted that the presentation of symptoms is highly variable between patients, so there are criticisms of this method of evaluation [64].  
However, this standardization is crucial to ensure consistent and precise diagnoses and treatments in different healthcare settings.  
Specifically, the DSM-V encompasses mental health disorders that span a spectrum from widely recognized disorders such as depression and anxiety to less common conditions such as catatonia. Associated with each disorder is a series of symptoms and diagnostic criteria that cumulatively present hundreds of distinct symptoms and criteria. These criteria are meticulously described, specifying the nature, duration, and impact of symptoms on the functioning of individuals. Primarily, the DSM-V assists clinicians and researchers in diagnosing mental disorders, ensuring that diagnoses are uniform and reflect the latest scientific understanding.  
To develop a Bayesian network that models the relationships between symptoms and conditions, we created a database based on the DSM-V as our primary source. We methodically reviewed each condition listed in the diagnostic manual, extracting and simplifying the descriptions of associated symptoms. This process involved distilling the complex and detailed symptom criteria into a more accessible format that can be readily understood by patients.  
We designed the network structure to link symptoms (\(s\)) with conditions (\(d\)). Our network is made up of 42 conditions and 114 symptoms.  
To generate the necessary Conditional Probability Tables (CPTs) to calculate probabilities within the network, we used the method of expert elicitation [65]. This is a common method for obtaining probabilities when data are scarce or unavailable. We asked a professional psychologist to assess the likelihood that a symptom manifests given a condition on a scale of three categories: necessary, common, and uncommon.  
These probabilities align with the QMR-DT network requirements, which will be explained later.  
An example of one of these rates is the following: within sleep disorders, the symptom of difficulty initiating or maintaining sleep was classified as necessary, as it is a defining characteristic of the condition, whereas the symptom of low mood was classified as common.  
We then assign probabilities to these categories in the CPT as follows: Necessary \(\rightarrow\)\(p(s|d)=1\), Common \(\rightarrow\)\(p(s|d)=0.7\), and Uncommon \(\rightarrow\)\(p(s|d)=0.3\).  
With this information, we developed a Bayesian network encompassing 42 conditions and 114 symptoms.