The impact of machine learning techniques in the study of bipolar disorder
Diego Librenza-Garcia, Bruno Jaskulski Kotzian, Jessica Yang, Benson Mwangi, Bo Cao, Luiza Nunes Pereira Lima, Mariane Bagatin Bermudez, Manuela Vianna Boeira, Flávio Kapczinski, Ives Cavalcante Passos,
Neuroscience and biobehavioral reviews, July 21, 2017
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.
Copyright © 2017 Elsevier Ltd. All rights reserved.
Pubmed Link: 28728937