Deep learning for the prediction of clinical outcomes in internet-delivered CBT for depression and anxiety.

ABSTRACT: In treating depression and anxiety, just over half of all clients respond. Monitoring and obtaining early client feedback can allow for rapidly adapted treatment delivery and improve outcomes. This study seeks to develop a state-of-the-art deep-learning framework for predicting clinical outcomes in internet-delivered Cognitive Behavioural Therapy (iCBT) by leveraging large-scale, high-dimensional time-series data of client-reported mental health symptoms and platform interaction data. We use de-identified data from 45,876 clients on SilverCloud Health, a digital platform for the psychological treatment of depression and anxiety. We train deep recurrent neural network (RNN) models to predict whether a client will show reliable improvement by the end of treatment using clinical measures, int

SUBMITTER: Prasad N 

PROVIDER: S-EPMC10681250 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature