Exploration of machine learning techniques in predicting multiple sclerosis disease course.

Journal: PloS one

Published Date: Apr 5, 2017

OBJECTIVE: To explore the value of machine learning methods for predicting multiple sclerosis disease course.

Yijun Zhao

Department of Computer Science, Tufts University, Medford, Massachusetts, United States of America.
Brian C Healy

Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.
Dalia Rotstein

Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.
Charles R G Guttmann

Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.
Rohit Bakshi

Laboratory for Neuroimaging Research, Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Howard L Weiner

Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.
Carla E Brodley

Department of Computer Science, Tufts University, Medford, Massachusetts3now with the College of Computer and Information Science, Northeastern University, Boston, Massachusetts.
Tanuja Chitnis

Partners MS Center, Brigham and Women's Hospital, Brookline, Massachusetts, United States of America.

Adult Brain Disease Progression Female Humans Logistic Models Machine Learning Magnetic Resonance Imaging Male Multiple Sclerosis Neuroimaging Sensitivity and Specificity Support Vector Machine

View on PubMed Access via DOI PubMed (28379999)