  The nature of mental illness remains a conundrum. Traditional disease
categories are increasingly suspected to mis-represent the causes underlying
mental disturbance. Yet, psychiatrists and investigators now have an
unprecedented opportunity to benefit from complex patterns in brain, behavior,
and genes using methods from machine learning (e.g., support vector machines,
modern neural-network algorithms, cross-validation procedures). Combining these
analysis techniques with a wealth of data from consortia and repositories has
the potential to advance a biologically grounded re-definition of major
psychiatric disorders. Within the next 10-20 years, incoming patients could be
stratified into distinct biological subgroups that cut across classical
diagnostic boundaries. In a new era of evidence-based psychiatry tailored to
single patients, objectively measurable endophenotypes could allow for
individualized prediction of early diagnosis, treatment selection, and dosage
adjustment to reduce the burden of disease. This primer aims to introduce
clinicians and researchers to the opportunities and challenges in bringing
machine intelligence into psychiatric practice.
