Data-driven analysis using multiple self-report questionnaires to identify college students at high risk of depressive disorder.

ABSTRACT: Depression diagnosis is one of the most important issues in psychiatry. Depression is a complicated mental illness that varies in symptoms and requires patient cooperation. In the present study, we demonstrated a novel data-driven attempt to diagnose depressive disorder based on clinical questionnaires. It includes deep learning, multi-modal representation, and interpretability to overcome the limitations of the data-driven approach in clinical application. We implemented a shared representation model between three different questionnaire forms to represent questionnaire responses in the same latent space. Based on this, we proposed two data-driven diagnostic methods; unsupervised and semi-supervised. We compared them with a cut-off screening method, which is a traditional diagnostic metho

SUBMITTER: Choi B 

PROVIDER: S-EPMC7217968 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature