A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnaires (2023)

Abstract: Social media is one of the most highly sought resources for analyzing characteristics of the language by its users. In particular, many researchers utilized various linguistic features of mental health problems from social media. However, existing approaches to detecting mental disorders face critical challenges, such as the scarcity of high-quality data or the trade-off between addressing the complexity of models and presenting interpretable results grounded in expert domain knowledge. To address these challenges, we design a simple but flexible model that preserves domain-based interpretability. We propose a novel approach that captures the semantic meanings directly from the text and compares them to symptom-related descriptions. Experimental results demonstrate that our model outperforms relevant baselines on various mental disorder detection tasks. Our detailed analysis shows that the proposed model is effective at leveraging domain knowledge, transferable to other mental disorders, and providing interpretable detection results.

## introduction mental health problems, a significant challenge in public healthcare, are usually accompanied by distinct symptoms, such as loss of interest or appetite, depressed moods, or excessive anxiety. as these symptoms can often be expressed over social media, detecting mental health conditions using social media text has been studied extensively @xcite @xcite @xcite @xcite . such approaches could give rise to a monitoring system that provides clinical experts with information about possible mental crises. to automatically identify mental health problems, traditional approaches focus on finding linguistic patterns and styles from the language of * corresponding author psychiatric patients. utilizing these features, statistical models can explain the correlation between linguistic factors and mental illnesses. however, these approaches suffer from increased complexity of models, necessitating pipelines of steps, from engineering features to producing results. by contrast, more recent works have employed strong pretrained models, which allow a direct use of raw data and simplify model development @xcite . while such end-to-end approaches may be effective at achieving higher performance, they often lack domain-based interpretation, which is essential for decision-support systems @xcite . hence, there is a trade-off between providing interpretable predictions based on domain knowledge and the simplicity of the models. the lack of a sufficient sample size for highquality data is another challenge in the clinical domain @xcite . despite the availability of diverse datasets and methods for detecting mental disorders, most of them aim primarily at identifying only clinical depression. to tackle such a problem, recent studies have focused on developing transferable linguistic features that can be used for the detection of various mental disorders @xcite . however, the linguistic features that are trained on a particular dataset may not be fully transferable to a different task @xcite . others utilized symptom-related features that are more common properties of psychiatric patients, resulting in generalizability of depression detection @xcite . despite this improvement, however, their approach still faces challenges because they rely on pipelined methods using manually-defined symptom patterns. such symptom patterns for depression detection lack flexibility as they cannot be easily adapted to other mental disorders. in addition, the pipeline approach with symptom extraction is quite complex to implement. it involves multiple steps, designing symptom patterns, training a symptom identification model, and detecting depression using the identified symptom patterns. to address these challenges, we propose to design a simple and more flexible approach that also preserves interpretability. we are motivated by the process that humans use to quickly learn related features, often by reading just a single explanation. for example, when people are reading depression questionnaires, they readily understand the questions and learn about symptoms that are related to depression, allowing them to self-diagnose their levels of depression. to this end, we employ the siamese network @xcite , which captures the semantic meaning of the text inputs and compares them directly to symptom-related descriptions. this process is simple since they find symptom-related clues directly from the input, rather than relying on hand-engineered features or intermediate models. our proposed model, multi-head siamese network (mhs), can be easily adapted to other mental illness domains by simply replacing the symptomrelated descriptions. in addition, our model is designed to capture the distinct features of each symptom using multiple heads. by examining the learned weights of each symptom head, our model gives rise to human-understandable interpretations. we evaluate the performance of our model, detecting texts containing mental health problems on four mental disorders. furthermore, the detailed analysis of the proposed model shows its efficiency in utilizing symptom-related knowledge, its ability to be applied to different mental disorders, and its interpretable reasoning for detected results. ## related work social media are commonly used for mental health research because of the ease of access to various aspects of human behavior studies. similarly to other nlp domains, pre-trained language models, such as bert @xcite , are widely used for identifying mental health problems @xcite @xcite . others have presented interpretable detection methods for the mental health domain based on linguistic features @xcite . various efforts have also been made to study such linguistic features accompanying mental illness, such as differences in word usage @xcite @xcite , or in syntactic features @xcite @xcite . some studies address the differences between sentiments or emotional aspects @xcite @xcite , or differences in topics @xcite . the linguistic features are also used for transferable methods across other mental disorders @xcite , focusing on the fact that a large number of studies have been done primarily on depression @xcite @xcite @xcite @xcite , compared to other disorders, such as anxiety disorder @xcite , anorexia @xcite , or schizophrenia @xcite . however, such linguistic features do not generalize well to new user groups. for example, de choudhury et al. (2017), @xcite found that the linguistic styles may vary to their backgrounds. in addition, @xcite found that a model trained on a particular dataset does not always generalize to others. to handle such a generalization problem, @xcite and @xcite focused on the shared and general properties (i.e., symptoms) of a mental health problem. however, unlike ours, which captures the symptom features directly from raw data, these methods require additional steps for learning symptom-related features. in this paper, we use the siamese network @xcite , based on one-shot learning, exploited recently for simple networks @xcite . we utilize the symptom descriptions sourced from dsm-5 (american psychiatric association, 2013) to make our model learn symptom-related knowledge. ## methodology in this section, we introduce our simple but flexible modeling for leveraging clinical questionnaires. our model aims to detect texts with mental illness episodes based on the presence of symptom-related features just by a single component. an overview of our network is shown in figure 1 . we designed our model based on the siamese network @xcite . as with the original siamese neural network, our model also contains a single feature extractor with shared parameters. the extractor directly obtains features from contextualized embeddings generated by sentence encoders. then, employing the similarity function, we compare the similarity to see the presence of symptom-related features from the target text. in addition, we apply multi-headed learning to the original siamese network, repeating the comparison process for each distinct symptom. we describe the detailed structure in the following subsections. ## conclusion in this paper, we proposed a simple but flexible model for detecting texts containing contents of mental health problems. our model outperformed the state-of-the-art models and achieved humaninterpretable results over symptoms regarding mental disorders. the proposed model demonstrates an exceptional ability to utilize domain knowledge as it is designed to capture relevant features from texts directly. experimental results also indicate that mhs can quickly adapt to other mental disorder domains by simply replacing symptom descriptions. the scope of this paper was limited to the investigation of four mental disorder detection tasks. nevertheless, this approach can be extended to other mental health conditions as long as the symptom-relevant questionnaires are provided accordingly.