Machine learning for precision psychiatry: Opportunites and challenges
Résumé
The nature of mental illness remains a conundrum. Traditional disease categories are increasingly
suspected to misrepresent 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
redefinition of major psychiatric disorders. Increasing evidence suggests that data-derived subgroups
of psychiatric patient can better predict treatment outcomes than DSM/ICD diagnoses. In a new era
of evidence-based psychiatry tailored to single patients, objectively measurable endophenotypes
could allow for early disease detection, individualized 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.
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