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Abstract
Machine learning approaches for clinical psychology and psychiatry explicitly focus
on learning statistical functions from multidimensional data sets to make generalizable
predictions about individuals. The goal of this review is to provide an accessible
understanding of why this approach is important for future practice given its potential
to augment decisions associated with the diagnosis, prognosis, and treatment of people
suffering from mental illness using clinical and biological data. To this end, the
limitations of current statistical paradigms in mental health research are critiqued,
and an introduction is provided to critical machine learning methods used in clinical
studies. A selective literature review is then presented aiming to reinforce the usefulness
of machine learning methods and provide evidence of their potential. In the context
of promising initial results, the current limitations of machine learning approaches
are addressed, and considerations for future clinical translation are outlined.