Machine learning and big data analytics in bipolar disorder : A position paper from the International Society for Bipolar Disorders Big Data Task Force

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dc.contributor.author Passos, Ives C.
dc.contributor.author Ballester, Pedro L.
dc.contributor.author Barros, Rodrigo C.
dc.contributor.author Librenza-Garcia, Diego
dc.contributor.author Mwangi, Benson
dc.contributor.author Birmaher, Boris
dc.contributor.author Brietzke, Elisa
dc.contributor.author Hajek, Tomas
dc.contributor.author Lopez Jaramillo, Carlos
dc.contributor.author Mansur, Rodrigo B.
dc.contributor.author Alda, Martin
dc.contributor.author Haarman, Bartholomeus C. M.
dc.contributor.author Isometsa, Erkki
dc.contributor.author Lam, Raymond W.
dc.contributor.author McIntyre, Roger S.
dc.contributor.author Minuzzi, Luciano
dc.contributor.author Kessing, Lars V.
dc.contributor.author Yatham, Lakshmi N.
dc.contributor.author Duffy, Anne
dc.contributor.author Kapczinski, Flavio
dc.date.accessioned 2020-01-10T12:32:01Z
dc.date.available 2020-01-10T12:32:01Z
dc.date.issued 2019-11
dc.identifier.citation Passos , I C , Ballester , P L , Barros , R C , Librenza-Garcia , D , Mwangi , B , Birmaher , B , Brietzke , E , Hajek , T , Lopez Jaramillo , C , Mansur , R B , Alda , M , Haarman , B C M , Isometsa , E , Lam , R W , McIntyre , R S , Minuzzi , L , Kessing , L V , Yatham , L N , Duffy , A & Kapczinski , F 2019 , ' Machine learning and big data analytics in bipolar disorder : A position paper from the International Society for Bipolar Disorders Big Data Task Force ' , Bipolar Disorders , vol. 21 , no. 7 , pp. 582-594 . https://doi.org/10.1111/bdi.12828
dc.identifier.other PURE: 127745717
dc.identifier.other PURE UUID: cdbde6b0-9d46-4a90-902f-12ba78471878
dc.identifier.other WOS: 000486760700001
dc.identifier.uri http://hdl.handle.net/10138/309249
dc.description.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. en
dc.format.extent 13
dc.language.iso eng
dc.relation.ispartof Bipolar Disorders
dc.rights unspecified
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject big data
dc.subject bipolar disorder
dc.subject data mining
dc.subject deep learning
dc.subject machine learning
dc.subject personalized psychiatry
dc.subject predictive psychiatry
dc.subject risk prediction
dc.subject MOOD DISORDERS
dc.subject PREDICTING SUICIDALITY
dc.subject LITHIUM RESPONSE
dc.subject RISK
dc.subject DEPRESSION
dc.subject SCHIZOPHRENIA
dc.subject ASSOCIATION
dc.subject CLASSIFICATION
dc.subject SYMPTOMS
dc.subject NEUROPROGRESSION
dc.subject 3112 Neurosciences
dc.subject 3124 Neurology and psychiatry
dc.title Machine learning and big data analytics in bipolar disorder : A position paper from the International Society for Bipolar Disorders Big Data Task Force en
dc.type Review Article
dc.contributor.organization Department of Psychiatry
dc.contributor.organization HUS Psychiatry
dc.contributor.organization University of Helsinki
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1111/bdi.12828
dc.relation.issn 1398-5647
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion
dc.type.version publishedVersion

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