Characterization of Anxiety, Depression, and their Comorbidity from Texts of Social Networks
Depression has become a public health issue, and the high comorbidity rate with anxiety worsens the clinical picture. Early identification is crucial for decisions on the proper line of treatment. The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions, particularly depression. This paper explores deep learning techniques to develop an ensemble stacking classifier for the automatic identification of depression, anxiety, and their comorbidity, using a self-diagnosed dataset extracted from Reddit. At the lowest level, binary classifiers make predictions about specific disorders, outperforming all baseline models. A meta-learner explores these weak classifiers as a context for reaching a multi-label decision, achieving a Hamming Loss of 0.29 and Exact Match Ratio of 0.47. We performed a qualitative analysis using SHAP, which confirmed the relationship between the influential features and symptoms of these disorders.
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