A Deep Learning Approach to Mining the Relationship of Depression Symptoms and Treatments for Prediction and Recommendation

Abstract

Background: Behavior regulation and clinical intervention have a significant effect on depression treatments. This study aims to make a comparison between behavior regulation and clinical intervention for depression based on a large-scale dataset. Methods: We collect user-reported data from an online survey tool including depression symptoms, treatments and effectiveness of treatments (n = 91873). A deep learning approach is used to build an effective model to evaluate the effects on treatment methods for depression. The Skip-gram model is chosen to generate meaningful vector representations of symptoms and methods. Precision, recall and F1 score are calculated to evaluate the model performance. Results: Unidirectional model achieves higher F1 score than non-unidirectional model (0.71 vs. 0.63). The behavior regulation is better than the clinical intervention for mild depression symptoms. However, the clinical intervention for moderate or severe depression symptoms has obvious advantages. Conclusions: These experiments prove that the symptoms have unidirectional influence on the choice of regulatory methods. The behavior regulation and clinical treatment have different advantages for depression. These findings could help clinicians to choose better depression treatments.