Predicting the Treatment Outcomes of Antidepressants Using a Deep Neural Network of Deep Learning in Drug-Naive Major Depressive Patients.

ABSTRACT: Predicting the treatment response to antidepressants by pretreatment features would be useful, as up to 70-90% of patients with major depressive disorder (MDD) do not respond to treatment as expected. Therefore, we aim to establish a deep neural network (DNN) model of deep learning to predict the treatment outcomes of antidepressants in drug-naïve and first-diagnosis MDD patients during severe depressive stage using different domains of signature profiles of clinical features, peripheral biochemistry, psychosocial factors, and genetic polymorphisms. The multilayer feedforward neural network containing two hidden layers was applied to build models with tenfold cross-validation. The areas under the curve (AUC) of the receiver operating characteristic curves were used to evaluate the performa

SUBMITTER: Tsai PL 

PROVIDER: S-EPMC9146151 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature