JMIR Publications

ABSTRACT

Background:

Lack of quantifiable biomarkers is a major obstacle in diagnosing and treating depression. In adolescents, increasing suicidality during antidepressant treatment further complicates the problem.

Objective:

We sought to evaluate digital phenotypes for the diagnosis and treatment response of depression in adolescents through a newly developed smartphone application.

Methods:

Our study included 24 adolescents (15.4±1.4 years, 17 girls) with major depressive disorder (MDD) diagnosed with K-SADS-PL and 10 healthy controls (13.8±0.6 years, 5 girls). Depression status was evaluated using the Children’s Depression Rating Scale–Revised (CDRS-R) and CGI-S every week. After 1 week’s baseline data collection, MDD adolescents were treated with escitalopram in an 8 week, open-label trial. Both MDD and control groups were monitored for 5 weeks including the baseline data collection period. We applied deep learning approach for the analysis of data. Deep Neural Network (DNN) was employed for classification and NEural network with Weighted Fuzzy Membership functions (NEWFM) for feature selection.

Results:

We could predict the diagnosis of depression with training accuracy of 96.3% and 3-fold validation accuracy of 77%. Of 24 depressed adolescents, 10 responded to antidepressant treatment. Including data on medications taken by the MDD group, we predicted the treatment response of depressed adolescents with training accuracy of 94.2% and 3-fold validation accuracy of 76%.

Conclusions:

Our smartphone application demonstrated preliminary evidence of predicting diagnosis and treatment response in depressed adolescents. This is the first study to predict treatment response of MDD in adolescents, examining smartphone based objective data with deep learning approaches.