  Observation is an essential tool for understanding and studying human
behavior and mental states. However, coding human behavior is a time-consuming,
expensive task, in which reliability can be difficult to achieve and bias is a
risk. Machine learning (ML) methods offer ways to improve reliability, decrease
cost, and scale up behavioral coding for application in clinical and research
settings. Here, we use computer vision to derive behavioral codes or concepts
of a gold standard behavioral rating system, offering familiar interpretation
for mental health professionals. Features were extracted from videos of
clinical diagnostic interviews of children and adolescents with and without
obsessive-compulsive disorder. Our computationally-derived ratings were
comparable to human expert ratings for negative emotions,
activity-level/arousal and anxiety. For the attention and positive affect
concepts, our ML ratings performed reasonably. However, results for gaze and
vocalization indicate a need for improved data quality or additional data
modalities.
