Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts (2023)

Abstract: Existing Mental Disease Detection (MDD) research largely studies the detection of a single disorder, overlooking the fact that mental diseases might occur in tandem. Many approaches are not backed by domain knowledge (e.g., psychiatric symptoms) and thus fail to produce interpretable results. To tackle these issues, we propose an MDD framework that is capable of learning the shared clues of all diseases, while also capturing the specificity of each single disease. The two-stream architecture which simultaneously processes text and symptom features can combine the strength of both modalities and offer knowledge-based explainability. Experiments on the detection of 7 diseases show that our model can boost detection performance by more than 10%, especially in relatively rare classes.

## introduction mental disease foot_0 detection (mdd) is of great practical value and social benefits, since mental disorders can greatly affect sufferers' life quality @xcite . lots of practices @xcite indicate that social media posts, containing sufficient expressions about one's feelings and symptoms, can be an informative data source for text-based automatic mdd, which aims to predict whether a person suffers from certain mental diseases. however, traditional mdd methods @xcite process every post in the user's posting history, which can include many irrelevant or distracting posts. to avoid these noises, some prior works try to extract key posts by clustering @xcite or semantic similarity @xcite , but these heuristics can still introduce erroneous posts, affecting the subsequent mdd results. moreover, comorbidity of several mental disorders is common @xcite . for instance, 75% of depression patients in the surveyed population also suffer from anxiety disorder in their lifetime @xcite . some research @xcite further suggests that mental diseases lie along a spectrum, hence it is quite usual for one person to develop symptoms of several related mental disorders at the same time, and be diagnosed with multiple diseases. however, detecting multiple mental diseases simultaneously in the scenario of comorbidity is still under-explored. most existing works focus on the detection of a single common disorder, such as depression @xcite , ignoring the frequent comorbidity diagnosed in clinical practice. to address these limitations, we aim to explore an approach to detect multiple mental disorders simultaneously in a comorbidity dataset, in which the diagnosed users can have one or more disorders. for simplicity, we refer to this detection task as multiple mdd in this paper, and it can be viewed as a multi-label classification problem. intuitively, detecting multiple mental diseases can be challenging to resolve, as there are lots of overlapping clinical manifestations shared among different diseases, so the labels implicitly intersect with each other. pioneering works on multiple mdd @xcite commonly yield unsatisfying results as they simply use a shared model architecture for all diseases, which may not be strong enough to distinguish between diseases. @xcite shows the effectiveness of implementing psychiatric symptom knowledge on multiple mdd, which is more interpretable and can outperform previous pure-text methods due to its better clinical grounding. however it detects each disease separately, ignoring the inner correlations between diseases, leading to unsatisfying results on rarer diseases like ocd. in this work, we propose psyex (psychiatric experts), a multi-task learning framework that can simultaneously detect 7 mental disorders foot_1 with a shared backbone and disease-specific structures to leverage the common characteristics of all diseases while still being able to capture their distinctions. this framework consists of two phases (see figure 1 ). first, we utilize a symptom identification model that explicitly leverages psychiatric knowledge to obtain symptom features for the symptom stream. the symptom features are also used to select posts that show high symptom risks. then another risky post stream implicitly learns clues for mental diseases from the semantics of the selected posts foot_2 . therefore, these two streams can complement each other and improve interpretability based on domain knowledge. they are fed into the multiple mdd model which has 7 disease-specific psychiatric experts on top of a shared hierarchy network, so that the backbone can learn the shared knowledge be-tween diseases, while each expert can focus on the characteristics of each specific disease. experiments show that our method can achieve sota multiple mdd performance across 7 diseases and bring significant improvement to rarer diseases on which the baselines struggled heavily. our main contributions are: ## approach in this section, we introduce the proposed framework for multiple mdd, including symptom identification and a two-stream psychiatric experts model. first, we give the definition of multiple mdd here. given the posting history of a user with n posts: {p 1 , p 2 , ..., p n }, and a list of m potential mental diseases, the goal is to detect which disease(s) in the m candidates does the user suffer from. typically, this can be accomplished by training m separate models for each disease. here, we further propose a novel method that leverage a single model to effectively learn the distinctions and commonality among diseases for a better overall performance on all diseases. ## experiments in this section, we conduct experiments to ( 1 ) examine the performance of our two-stream psyex model compared with strong baselines; (2) validate the effectiveness of various design choices with ablation tests; (3) evaluate the model's generalizability on different datasets; ( 4 ) analyze the interpretability enabled by symptoms and diseasespecific "experts". ## related work in the literature, substantial efforts have been made to detect a certain mental disease. some of these works focus on leveraging features like tf-idf, liwc @xcite , and posting patterns @xcite for mdd. others apply various deep learning methods @xcite , as well as the contextualized embedding @xcite to improve the performance of classifiers. however, these methods often fail to generalize well @xcite and cannot provide explainable results due to lack of knowledge in the psychiatric domain. to tackle these issues, some works @xcite began to utilize symptom features, but they extract symptom features with unsupervised/weakly supervised methods, which isn't so reliable for the downstream mdd task. recent years, some works start to detect multiple mental disorders. @xcite proposed a massive reddit dataset smhd containing 9 mental disorders, followed by many subsequent studies based on this dataset, such as @xcite and @xcite . however, these works often directly apply a single-disease model to the multi-disease data (i.e., train the model for d times to obtain the results of d diseases), overlooking the correlation among multiple diseases, and thus fail to perform well on rarer diseases. with the emergence of large language models (llms), there exists a few studies utilizing llms for tasks like depression detection on social media @xcite or developing chatbots for depression diagnosis @xcite ), but we fail to find any existing work that simultaneously detects multiple mental disorders or for mental health symptom extraction. while we acknowledge llm's potential in mental health application, it needs further exploration and investigation as our preliminary results indicated that this remains a challenging task. ## conclusions in this work, we tackle the challenge of detecting multiple mental diseases simultaneously in the scenario of comorbidity, and propose a multiple mdd framework achieving sota performance in both binary and multi-label settings. we first apply risky post screening based on symptoms, providing reliable diagnostic basis for further disease detection. then, we propose a two-stream psychiatric experts model with a shared hierarchical encoder and disease-specific attention layers, which simultaneously process the symptom and text features to combine the advantage of both modality. we also explore the interpretability of psyex by providing a user-level analysis, and measuring the global symptom contribution to the detection of different diseases.