Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning

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• 作者：Mon-Ju Wu^a ; ¹ ; Benson Mwangi^a ; ¹ ; ^{benson.irungu@uth.tmc.edu} ; Isabelle E. Bauer^a ; Ives C. Passos^a ; Marsal Sanches^a ; Giovana B. Zunta-Soares^a ; Thomas D. Meyer^a ; Khader M. Hasan^b ; Jair C. Soares^a
• 关键词：Bipolar disorder ; Neuroimaging ; Neuropsychology ; Machine learning ; Heterogeneity ; Research Domain Criteria (RDoC) ; Big data
• 刊名：NeuroImage
• 出版年：2017
• 出版时间：15 January 2017
• 年：2017
• 卷：145
• 期：part_PB
• 页码：254-264
• 全文大小：1295 K
• 卷排序：145

文摘

An unsupervised machine learning method and neurocognitive data used to identify two phenotypes LASSO distinguished two phenotypes using neurocognitive data with 94% accuracy. Elastic Net validates differences of the two phenotypes using FA data with 76% accuracy. Healthy controls are further used to validate differences between the two phenotypes.