Machine Learning Prediction of Mental Health Strategy Selection in School-Aged Children Using Neurocognitive Data (Poster 30)

• In Event: AERA Poster Session Thursday 1:45 p.m.
  In Poster Session: Poster Session for Stress, Resilience, and Coping

Abstract

This study investigates the use of neurocognitive data for the development of predictive algorithms related to Dialectical Behavior Therapy (DBT) skill use in school based Digital Counseling Environments. 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. 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. Findings from this study illustrate that the use of neurocognitive data in school based environments can successfully predict outcomes, and increase understandings of socio-emotional progress in the classroom.

Authors

• Richard Lamb, University of Georgia

• Jonah B. Firestone, Washington State University - Tri-Cities

• Tosha L. Owens, East Carolina University

• Ikseon Choi, Emory University

• Amanda Kavner, Farmingdale State College - SUNY

• Norah Mansour Almusharraf, Prince Sultan University