Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study

The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (...

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Veröffentlicht in:	NPJ digital medicine Jg. 3; H. 1; S. 135 - 8
Hauptverfasser:	Zhao, Yijun, Wang, Tong, Bove, Riley, Cree, Bruce, Henry, Roland, Lokhande, Hrishikesh, Polgar-Turcsanyi, Mariann, Anderson, Mark, Bakshi, Rohit, Weiner, Howard L., Chitnis, Tanuja
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 16.10.2020 Nature Publishing Group Nature Portfolio
Schlagworte:	692/617/375/1411/1666 692/617/375/1666 Algorithms Biomedicine Biotechnology Datasets Digital technology Health informatics Machine learning Medicine Medicine & Public Health Multiple sclerosis Multiple sclerosis
ISSN:	2398-6352, 2398-6352
Online-Zugang:	Volltext
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