Machine learning and big data in psychiatry: toward clinical applications.

TL;DR: There have been crucial advances in psychiatric diagnosis and treatment in recent decades but there is considerable need for further improvements in assessment and intervention, and improvements will likely not be achieved by any specific paradigm shifts in psychiatric practice and research, but rather by incremental progress and iterative integration.

TL;DR: It is argued that the brain is a computational organ that will require a computational framework for an understanding of the illnesses arising from it, and recent advances in theory-driven work are reviewed.

TL;DR: A recent systematic review of machine learning approaches in predicting mental health problems with concrete recommendations on the potential future research and development of applying machine learning in the mental health field is presented.

TL;DR: This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them and identifies exemplars from the emerging literature on artificial intelligence and precision approaches to treatment.

TL;DR: A recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy and interpersonal psychotherapy, is replicated, promoting the development of a treatment selection approach that can be used in regular mental health care, advancing the goals of personalized medicine.

TL;DR: It is argued that there are currently no brain imaging biomarkers that are clinically useful for establishing diagnosis or predicting treatment outcome in mood disorders.

TL;DR: Increased pretreatment rACC theta activity represents a nonspecific prognostic marker of treatment outcome, and predicted greater depressive symptom improvement, even when controlling for clinical and demographic variables previously linked with treatment outcome.