Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis

Current care in multiple sclerosis (MS) primarily relies on infrequently obtained data such as magnetic resonance imaging, clinical laboratory tests or clinical history, resulting in subtle changes that may occur between visits being missed. Mobile technology enables continual collection of data and...

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Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 18209 - 12
Main Authors:	Roy, Subhrajit, Mincu, Diana, Proleev, Lev, Ghate, Chintan, Graves, Jennifer S., Steiner, David F., Hartsell, Fletcher Lee, Heller, Katherine
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 25.05.2025 Nature Publishing Group Nature Portfolio
Subjects:	692/1807/1486 692/1807/2781 692/617/375/1666 Adult Autoimmune diseases Chronic illnesses Data collection Deep learning Disease Progression Fatigue Female Humanities and Social Sciences Humans Laboratory tests Longitudinal Studies Machine Learning Magnetic Resonance Imaging Male Medical laboratories Mental depression Middle Aged Mobile Applications multidisciplinary Multiple sclerosis Multiple Sclerosis - diagnosis Multiple Sclerosis - physiopathology Nervous system Observational studies Portable computers Retrospective Studies Science Science (multidisciplinary) Severity of Illness Index
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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