Machine learning and big data in psychiatry: toward clinical applications. (April 2019) - Rutledge, Robb B; Chekroud, Adam M; Huys, Quentin JM
Highlights: The combination of data-driven machine learning and theory-driven computational models holds great promise for psychiatry. Machine-learning analyses of existing data can yield predictors and illness phenotypes. Online and smartphone designs provide large and potentially highly relevant data. Drug development pipelines can be adapted to evaluate computational tools. Algorithmic biases must be avoided, particularly because of the harm they can cause in vulnerable populations. Abstract : Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis. Data-driven machine-learning approaches can make predictions from high-dimensional data and are generally agnostic as to the underlying mechanisms. Here, we review recent advances in the use of big data and machine-learning approaches toward the aim of alleviating the suffering that arises from psychiatric disorders.