中国科学院自动化研究所机构知识库

Background: Behavior regulation and clinical intervention have a significant ef-fect  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 de-pression  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 interven-tion for mild depression symptoms. However, the clinical intervention for mod-erate or severe depression symptoms has obvious advantages.
Conclusions: These experiments prove that the symptoms have unidirectional in-fluence 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.