Clinical psychoinformatics: a novel approach to behavioral states and mental health care driven by machine learning

Machine learning (ML) is a branch of artificial intelligence technology that has received considerable attention in recent years. It is a computational strategy to discover the regularities inherent in multidimensional data sets, allowing us to build predictive models focused on individual states. Therefore, it may help increase the efficiency and sophistication of assessment and aid the selection of optimal intervention methods in clinical practice, including cognitive behavioral therapy. In this paper, we first review the framework of the ML approach, its differences from statistics, and its features. Subsequently, we summarize the main research topics where ML approaches have been applied in the field of mental health and introduce some examples of their applications that may contribute to research in clinical psychology and cognitive behavioral therapy. Finally, the limitations of the ML approach are discussed, as well as its potential for future applications.