On the Use of Bayesian Artificial Intelligence for Hypothesis Generation in Psychiatry

In this study, I introduce the use of Bayesian Artificial Intelligence, namely through the probabilistic and structure learning of Bayesian Network models, for hypothesis generation in psychiatry. Bayesian Networks are directed acyclic graphical models that allow researchers to account for complexity in multivariate data sets, as well as identify what is the likely causal direction in detected associations. This in turns leads to more effective designs for confirmatory studies in clinical settings, that go beyond association studies and can provide meaningful impact in clinical practice. As an example, I use three different data sets to highlight several frameworks for hypothesis generation. Bayesian Networks are useful models since the early stages of knowledge generation in psychiatry, and they can be easily adopted by most applied and clinical researchers for use in quantitative studies.