Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-63888-x |
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| author | Subhrajit Roy Diana Mincu Lev Proleev Chintan Ghate Jennifer S. Graves David F. Steiner Fletcher Lee Hartsell Katherine Heller |
| author_facet | Subhrajit Roy Diana Mincu Lev Proleev Chintan Ghate Jennifer S. Graves David F. Steiner Fletcher Lee Hartsell Katherine Heller |
| author_sort | Subhrajit Roy |
| collection | DOAJ |
| description | Abstract 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 can pave the path for predicting complex aspects of MS such as symptoms and disease courses. To this end, we conducted a first-of-its-kind observational study called MS Mosaic. First, we developed and publicly launched a mobile app for collecting longitudinal data from MS subjects in the United States. Second, we ran the study across 3 years in order to capture complex patterns for this slow progressing disease. Finally, we retrospectively developed three classical ML methods and two deep learning models to accurately and continually predict the incidence of five high-severity symptoms (fatigue, sensory disturbance, walking instability, depression or anxiety and cramps/spasms) three months in advance. |
| format | Article |
| id | doaj-art-0c98e2d8297843b8838a8d9e2aa1da1f |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0c98e2d8297843b8838a8d9e2aa1da1f2025-08-20T02:29:51ZengNature PortfolioScientific Reports2045-23222025-05-0115111210.1038/s41598-024-63888-xPerformance of machine learning models for predicting high-severity symptoms in multiple sclerosisSubhrajit Roy0Diana Mincu1Lev Proleev2Chintan Ghate3Jennifer S. Graves4David F. Steiner5Fletcher Lee Hartsell6Katherine Heller7Google ResearchGoogle ResearchGoogle ResearchGoogle ResearchDepartment of Neurosciences, University of California, San DiegoGoogle HealthDuke University School of MedicineGoogle ResearchAbstract 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 can pave the path for predicting complex aspects of MS such as symptoms and disease courses. To this end, we conducted a first-of-its-kind observational study called MS Mosaic. First, we developed and publicly launched a mobile app for collecting longitudinal data from MS subjects in the United States. Second, we ran the study across 3 years in order to capture complex patterns for this slow progressing disease. Finally, we retrospectively developed three classical ML methods and two deep learning models to accurately and continually predict the incidence of five high-severity symptoms (fatigue, sensory disturbance, walking instability, depression or anxiety and cramps/spasms) three months in advance.https://doi.org/10.1038/s41598-024-63888-x |
| spellingShingle | Subhrajit Roy Diana Mincu Lev Proleev Chintan Ghate Jennifer S. Graves David F. Steiner Fletcher Lee Hartsell Katherine Heller Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis Scientific Reports |
| title | Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis |
| title_full | Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis |
| title_fullStr | Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis |
| title_full_unstemmed | Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis |
| title_short | Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis |
| title_sort | performance of machine learning models for predicting high severity symptoms in multiple sclerosis |
| url | https://doi.org/10.1038/s41598-024-63888-x |
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