Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force. (18th September 2019)

• Record Type: Journal Article
• Authors: Passos, Ives C.; Ballester, Pedro L.; Barros, Rodrigo C.; Librenza‐Garcia, Diego; Mwangi, Benson; Birmaher, Boris; Brietzke, Elisa; Hajek, Tomas; Lopez Jaramillo, Carlos; Mansur, Rodrigo B.; Alda, Martin; Haarman, Bartholomeus C. M.; Isometsa, Erkki; Lam, Raymond W.; McIntyre, Roger S.; Minuzzi, Luciano; Kessing, Lars V.; Yatham, Lakshmi N.; Duffy, Anne; Kapczinski, Flavio
• Is Part Of: Bipolar disorders. Volume 21:Number 7(2019)
• Page Start: 582
• Page End: 594
• Publication Date: 2019-09-18
• Subjects: big data -- bipolar disorder -- data mining -- deep learning -- machine learning -- personalized psychiatry -- predictive psychiatry -- risk prediction; Manic-depressive illness -- Periodicals; Depression, Mental -- Periodicals; 616.895
• DOI: 10.1111/bdi.12828
• Languages: English
• ISSNs: 1398-5647

Abstract

Abstract: 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.