CURE : Context- and Uncertainty-Aware Mental Disorder Detection (2024)

Abstract: As the explainability of mental disorder detection models has become important, symptom-based methods that predict disorders from identified symptoms have been widely utilized. However, since these approaches focused on the presence of symptoms, the context of symptoms can be often ignored, leading to missing important contextual information related to detecting mental disorders. Furthermore, the result of disorder detection can be vulnerable to errors that may occur in identifying symptoms. To address these issues, we propose a novel framework that detects mental disorders by leveraging symptoms and their context while mitigating potential errors in symptom identification. In this way, we propose to use large language models to effectively extract contextual information and introduce an uncertainty-aware decision fusion network that combines predictions of multiple models based on quantified uncertainty values. To evaluate the proposed method, we constructed a new Korean mental health dataset annotated by experts, named KoMOS. Experimental results demonstrate that the proposed model accurately detects mental disorders even in situations where symptom information is incomplete.

## introduction mental disorders have become an urgent global issue. the number of individuals suffering from mental disorders exceeds one billion, approximately 16 percent of the world's population @xcite . such severity has in turn leveraged the importance of detecting mental disorders, which determines whether an individual can be in a danger of mental illness or not. social media has been considered as one of the key sources widely used in mental disorder detection research, as its anonymity property encourages individuals with mental illness to reveal their mental health * corresponding authors. status or self-disclosure rarely concerning about social stigma @xcite @xcite . connecting individuals to many peers who share similar experiences to mental health experts online without temporal and space restriction, social media has populated a deluge of data, which has been widely used for mental disorder detection @xcite @xcite . the growing importance of mental disorder detection and the availability of abundant data have attracted research communities to develop diverse deep learning models for the mental disorder detection task @xcite @xcite . unfortunately, despite their significant efforts in performance improvement, the black-box property of deep learning models can be a limitation in providing interpretation of the model results or evidences of a decision, which degrades the trustworthiness @xcite . leveraging the importance of explainability of the model results in mental disorder detection, there have been a few attempts that find psychiatric symptoms in detect mental disorders @xcite @xcite . these approaches, denoted as symptom-based models, consist of a two-step pipeline in general: (i) a symptom identification from user-generated posts, which computes a vector representing the presence or likelihood of symptoms, and (ii) a disease detection to predict mental disorders using the calculated symptom vector. note that only the symptom vector is used in the second step. compared to postbased approaches, these approaches not only detect mental disorders with high performance, but also provide an interpretability of the model results as the symptom vector can be used in interpreting how the decision was made by the likelihood of the symptoms @xcite @xcite . however, relying solely on a symptom vector in the symptom-based approaches can be limited in capturing context information around symptoms and diseases, resulting in inaccurate decision for mental disorder. according to the dsm-5 (diagnostic and statistical manual of mental disorders), it is suggested to consider not only the indicated symptoms, but also contextual information of symptoms, including duration, frequency, and causes, for diagnosis. as figure 1 illustrates, for example, a criterion for diagnosing major depressive disorder includes experiencing a depressed mood during the same 2-week period (american psychiatric @xcite . thus, the post "i feel sad because i failed my exam yesterday." should be decided as non-depression while the post "i have been feeling sad for a month." indicates potential depression. unfortunately, the symptom-based models are likely to determine both posts as depression disorder, since the symptom is explicitly indicated in the two posts. furthermore, uncertainty of a symptom-based model in computing symptom vectors should be considered in detecting mental disorders. since the symptom-based models depend on only symptom vectors calculated from user posts, a symptom vector with low confidence can result in inaccurate decision. note that recent work has highlighted the risk of the current symptom identification models showing suboptimal accuracy due to difficulty in capturing complex symptom expression from user posts @xcite @xcite . to address these limitations, we propose cure (context-and uncertainty-aware mental disorder detection), a novel approach for detecting mental disorders that (i) uses not only the presence or likelihood of symptoms, but also their contextual information and (ii) reduces the potential uncertainty originated from the symptom identification. to this end, the proposed model is designed to cooperates with a large language model (llm) showing a capability in natural language understanding to effectively extract contextual factors pre-defined from the guidance of psychiatrists. consisting of five different sub-models and the uncertainty-aware decision-fusion network with spectral-normalized neural gaussian process (sngp) @xcite , the proposed model can detect mental disorders accurately even when symptoms are hardly captured from a given post. our evaluation with the newly collected and annotated dataset named as komos (korean mental health dataset with mental disorder and symptoms labels), which contains 6,349 q&a pairs created through the interaction between a user and an expert for mental health demonstrates that the proposed model outperforms the state-ofthe-art models, showing robust performance even when symptom identification is incorrect. we summarize the contributions of this paper as follows. ## dataset construction in this section, we describe how to build komos, a novel korean mental health dataset with mental disorder and symptoms labels. ## the model defining the mental disorder detection task as a multi-label classification problem, the model takes a user's post as an input and predicts five labels (4 disease and non-disease) independently. in this section, we describe the proposed model in detail. ## conclusion in this paper, we proposed a novel approach in mental disorders detection. our model uses contextual information for more accurate detection and employs an uncertainty-aware decision fusion network to reduce errors. additionally, we created komos, the first mental health dataset in korea, which will be publicly available to authorized researchers. experiments show our approach outperforms existing methods. we believe the proposed model can provide early intervention and support by quickly detecting mental disorders on social media.