Machine learning prediction of mental health strategy selection in school aged children using neurocognitive data

This study investigates the use of neurocognitive data collected via functional near infrared spectroscopy (fNIRS) for the development of predictive algorithms related to client Dialectical Behavior Therapy (DBT) skill use in Digital Counseling Environments (DCEs). Specifically, the aim of this study is to examine the utility of using neurocognitive data in combination with a machine learning algorithm to predict client selection of strategies in a virtual environment. Participants were recruited from a rural school located in the United States (n = 50). Student participants engaged in one of three DBT skill development conditions: time-delay-control, face-to-face, and virtual reality-based. Neurocognitive responses in the form of ratios of oxygenated and deoxygenated blood in neural tissue (hemodynamic response) were collected during the learning phase within each condition. The data was then used to train and develop a model to predict the success of client selection of DBT strategies when presented with the opportunity in an assessment phase. The average predictive accuracy of the resultant algorithm was ∼83%. Results also illustrated the potential to capture changes in cognition via hemodynamic response as they occur during DBT skill development in near real-time (∼300 ms). Findings from this study illustrate that the use of neurocognitive data in DCEs may be used to successfully predict client outcomes and increase the quality and reliability of counselor assessments of client progress, aid in the development of artificially intelligent counselors, and improve counselors use of client-based analytics in face-to-face and digital counseling environments.