Computational approaches in mental health

In the past few decades, technology has played a considerable role in medical research. For example, advances in machine learning algorithms have allowed for automatic diagnosis of medical conditions, through techniques involving neuroimaging data, electronic health records and physiological biomarkers. Research in the past few decades has also resulted in a greater understanding of the causes of medical conditions, such as through biophysical models of the body and computational genetics. In parallel with other medical fields, computational models in psychology have opened new avenues of diagnosis and prognosis of mental health conditions. In this article we describe common mental health conditions, and discuss computational approaches in mental health—including drift-diffusion modeling, reinforcement learning and machine learning approaches.