OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. METHOD: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. RESULTS: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. CONCLUSION: Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

Copenhagen Affective Disorder Research Center Psychiatric Center Copenhagen Copenhagen University Hospital Copenhagen Denmark

Department of Psychiatry and Behavioral Sciences UT Center of Excellence on Mood Disorders McGovern Medical School The University of Texas Health Science Center at Houston Houston TX USA

Department of Psychiatry and Behavioural Neurosciences McMaster University Hamilton ON Canada

Department of Psychiatry Dalhousie University Halifax NS Canada

Department of Psychiatry Queen's University School of Medicine Kingston ON Canada

Department of Psychiatry University Medical Center Groningen University of Groningen Groningen The Netherlands

Department of Psychiatry University of British Columbia Vancouver BC Canada

Department of Psychiatry University of Helsinki and Helsinki University Central Hospital Helsinki Finland

Department of Psychiatry University of Toronto Toronto ON Canada

Department of Psychiatry Western Psychiatric Institute and Clinic University of Pittsburgh School of Medicine Pittsburgh PA USA

Laboratory of Molecular Psychiatry and Bipolar Disorder Program Programa de Pós Graduação em Psiquiatria e Ciências do Comportamento Hospital de Clínicas de Porto Alegre Universidade Federal do Rio Grande do Sul Porto Alegre Brazil

Mood Disorders Program Hospital Universitario San Vicente Fundación Medellín Colombia

Mood Disorders Psychopharmacology Unit University Health Network University of Toronto Toronto ON Canada

National Institute of Mental Health Klecany Czech Republic

Research Group in Psychiatry Department of Psychiatry Faculty of Medicine University of Antioquia Medellín Colombia

School of Technology Pontifícia Universidade Católica do Rio Grande do Sul Rio Grande do Sul Brazil

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What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group