Enhancing Diagnostic Precision: Using Large-Language Models to Evaluate Content Overlap in Mental Health Questionnaires (Preprint)

<sec> <title>BACKGROUND</title> Mental disorders are frequently evaluated using questionnaires, which have been developed over the past decades for the assessment of different conditions. Despite the abundance of these tools, it is an open research question whether different questionnaires measure the same construct of psychopathology. Previous studies that examined the content overlap required manual symptom labeling which is not entirely observer-independent and time-consuming. </sec> <sec> <title>OBJECTIVE</title> Here, we employed large-language models (LLMs) to analyze content overlap of mental health questionnaires in an observer-independent way and compare our results with clinical expertise. </sec> <sec> <title>METHODS</title> We analyzed questionnaires from a range of mental health conditions including adult depression (n=7), childhood depression (n=15), clinical high risk for psychosis (CHR-P; n=13), mania (n=7), obsessive-compulsive disorder (n=7), and sleep disorder (n=12). Two different LLM-based approaches were tested. First, we employed sentence Bidirectional Encoder Representations from Transformers (sBERT) to derive numerical representations (‘embeddings’) for each questionnaire item, which were then clustered using k-means to group semantically similar symptoms. Second, questionnaire items were prompted to a Generative Pre-trained Transformer (GPT) to identify underlying symptom clusters. Clustering results were compared to a manual categorization by experts using the adjusted rand index. Further, we assessed the content overlap within each diagnostic domain based on LLM-derived clusters. </sec> <sec> <title>RESULTS</title> We observed varying degrees of similarity between expert-based and LLM-based clustering across diagnostic domains. Overall, agreement between experts was higher than between experts and LLMs. Among the two LLM approaches, GPT showed greater alignment with expert ratings than sBERT, ranging from weak to strong similarity depending on the diagnostic domain. Using GPT-based clustering of questionnaire items to assess the content overlap within each diagnostic domain revealed a weak (CHR-P: 0.344) to moderate (adult depression: 0.574; childhood depression: 0.433; mania: 0.419; OCD: 0.450; sleep disorder: 0.445) content overlap of questionnaires. Compared to the studies that manually investigated content overlap among these scales, the results of this study exhibited variations, though these were not substantial. </sec> <sec> <title>CONCLUSIONS</title> These findings demonstrate the feasibility of using LLMs to objectively assess content overlap in diagnostic questionnaires. Notably, the GPT-based approach showed particular promise in aligning with expert-derived symptom structures. </sec>