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T1  - Machine learning and big data analytics in bipolar disorder : A position paper from the International Society for Bipolar Disorders Big Data Task Force 
SN  -  /  
UR  - http://hdl.handle.net/10138/309249 
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A1  - 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 
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Y1  - 2019 
LA  - eng 
AB  - 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 ... 
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KW  - big data; bipolar disorder; data mining; deep learning; machine learning; personalized psychiatry; predictive psychiatry; risk prediction; MOOD DISORDERS; PREDICTING SUICIDALITY; LITHIUM RESPONSE; RISK; DEPRESSION; SCHIZOPHRENIA; ASSOCIATION; CLASSIFICATION; SYMPTOMS; NEUROPROGRESSION; 3112 Neurosciences; 3124 Neurology and psychiatry 
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