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

Article English OPEN
Yijun Zhao; Brian C Healy; Dalia Rotstein; Charles R G Guttmann; Rohit Bakshi; Howard L Weiner; Carla E Brodley; Tanuja Chitnis;
  • Publisher: Public Library of Science (PLoS)
  • Journal: PLoS ONE,volume 12,issue 4 (issn: 1932-6203, eissn: 1932-6203)
  • Related identifiers: doi: 10.1371/journal.pone.0174866, pmc: PMC5381810
  • Subject: Magnetic Resonance Imaging | Research Article | Diagnostic Medicine | Lesions | Mathematics | Signs and Symptoms | Pathology and Laboratory Medicine | Diagnostic Radiology | Mathematical and Statistical Techniques | Radiology and Imaging | Artificial Intelligence | Clinical Medicine | Neurodegenerative Diseases | Physical Sciences | People and Places | Demography | Immunology | Neurology | Autoimmune Diseases | Demyelinating Disorders | Statistics (Mathematics) | Support Vector Machines | Biology and Life Sciences | Computer and Information Sciences | Multiple Sclerosis | Neuroscience | Forecasting | Research and Analysis Methods | Medicine | Machine Learning | Neuroimaging | Q | R | Imaging Techniques | Clinical Immunology | Science | Medicine and Health Sciences | Statistical Methods

Objective To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods 1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vect... View more
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