Clinical Applications of Machine Learning: A Novel Approach to Mental Health Care Driven by Digital Innovation

Abstract

Machine learning is a branch of artificial intelligence that has received considerable attention in recent years. It is a computational strategy for discovering regularities inherent in multidimensional data sets and allows us to build predictive models focused on individual states. Such models may help improve the efficiency and sophistication of assessments and aid in the selection of optimal intervention methods in clinical practice, including cognitive behavioral therapy. In this study, we first review the framework of the machine learning approach, identify its differences from statistics, and examine its features. Subsequently, we summarize the main research topics that have leveraged machine learning approaches in the field of mental health, and introduce some examples that may contribute to research in clinical psychology and cognitive behavioral therapy. Finally, we discuss the limitations of the machine learning approach, as well as its potential for future applications.