Multiple Sclerosis
What's New
Last Posted: May 07, 2024
- Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records.
Christina Felix et al. Digit Health 2024 1020552076241249286 - Artificial intelligence in multiple sclerosis management: Challenges in a new era.
Sebastián Rodríguez et al. Mult Scler Relat Disord 2024 86105611 - Validated, Quantitative, Machine Learning-Generated Neurologic Assessment of Multiple Sclerosis Using a Mobile Application.
Sharon Stoll et al. Int J MS Care 2024 26(2) 69-74 - Predicting multiple sclerosis severity with multimodal deep neural networks.
Kai Zhang et al. BMC Med Inform Decis Mak 2023 23(1) 255 - Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis.
Tuba Ekmekyapar et al. Diagnostics (Basel) 2023 13(19) - Innovations in Multiple Sclerosis Care: The Impact of Artificial Intelligence via Machine Learning on Clinical Research and Decision-Making.
Jacob Cartwright et al. Int J MS Care 2023 25(5) 233-241 - Machine learning-optimized Combinatorial MRI scale (COMRISv2) correlates highly with cognitive and physical disability scales in Multiple Sclerosis patients.
Erin Kelly et al. Front Radiol 2023 21026442 - The familial risk and heritability of multiple sclerosis and its onset phenotypes: A case-control study.
Graysen Steele Boles et al. Mult Scler 2023 13524585231185258 - Predictive models of multiple sclerosis-related cognitive performance using routine clinical practice predictors.
Andrés Labiano-Fontcuberta et al. Mult Scler Relat Disord 2023 76104849 - Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis.
Maryam Tayyab et al. Front Neurol 2023 141165267 - Polygenic risk score prediction of multiple sclerosis in individuals of South Asian ancestry.
Joshua R Breedon et al. Brain communications 2023 5(2) fcad041 - Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis.
Aleide Hoeijmakers et al. Scientific reports 2023 13(1) 1871 - Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach.
Ferrè Laura et al. Journal of personalized medicine 2023 13(1) - A Cross-Trait, Mendelian Randomization Study to Investigate Whether Migraine Is a Risk Factor for Multiple Sclerosis.
Horton Mary K et al. Neurology 2023 - Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones.
Creagh Andrew P et al. IEEE open journal of engineering in medicine and biology 2022 3202-210 - Machine learning for exploring neurophysiological functionality in multiple sclerosis based on trigeminal and hand blink reflexes.
Biggio Monica et al. Scientific reports 2022 12(1) 21078 - Multimodal-neuroimaging machine-learning analysis of motor disability in multiple sclerosis.
Rehák Bucková Barbora et al. Brain imaging and behavior 2022 - Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome.
Barile Berardino et al. Frontiers in robotics and AI 2022 9926255 - Familial autoimmunity in patients with idiopathic inflammatory myopathies.
Che Weng Ian et al. Journal of internal medicine 2022 - Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning.
Falet Jean-Pierre R et al. Nature communications 2022 9 (1) 5645
More
About ND PHGKB
Neurological Disorders (ND) PHGKB is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other information that address the public health impact and translation of genomic and other precision health discoveries into improved health outcomes related to neurological disorders....more
Content Summary
Common ND Related Topics
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Feb 1, 2024
- Page last updated:May 08, 2024
- Content source: