The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response medications or psychotherapies, order personalize their treatment choices. There is now a broad and growing interest the idea that we can develop models decisions using new statistical approaches from field of machine learning applying them larger volumes data. In this pursuit, there has been paradigm shift away experimental studies confirm refute specific hypotheses towards focus on overall explanatory power predictive model when tested new, unseen datasets. paper, review key predict outcomes psychiatry, ranging psychotherapies digital interventions neurobiological treatments. Next, some sources data are being used for development based learning, such as electronic health records, smartphone social media data, potential utility genetics, electrophysiology, neuroimaging cognitive testing. Finally, discuss how far come implementing prediction tools real-world clinical practice. Relatively few retrospective to-date include appropriate external validation procedures, even fewer prospective testing feasibility effectiveness models. Applications psychiatry face same ethical challenges posed by these techniques other areas medicine computer science, which here. short, nascent but important approach improve mental care, several suggest it may be working already.