The impact of machine learning techniques in the study of bipolar disorder: A systematic review

•Machine learning algorithms might provide models for clinical decisions at an individual level.•Machine learning techniques may contribute to improve diagnosis and prognosis orientation in BD.•Machine learning may aid to establish personalized care of patients with BD.•Unsupervised models may identify more monolithic clinical phenotypes of BD.

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation.