A machine learning approach to determine the risk factors for fall in multiple sclerosis
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1186/s12911-024-02621-0.pdf
Reference89 articles.
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2. Walton C, King R, Rechtman L, Kaye W, Leray E, Marrie RA, et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS. Mult Scler J. 2020;26(14):1816–21.
3. Gunn HJ, Newell P, Haas B, Marsden JF, Freeman JA. Identification of risk factors for falls in multiple sclerosis: a systematic review and meta-analysis. Phys Ther. 2013;93(4):504–13.
4. Filli L, Sutter T, Easthope CS, Killeen T, Meyer C, Reuter K, et al. Profiling walking dysfunction in multiple sclerosis: characterisation, classification and progression over time. Sci Rep. 2018;8(1):4984.
5. Gianni C, Prosperini L, Jonsdottir J, Cattaneo D. A systematic review of factors associated with accidental falls in people with multiple sclerosis: a meta-analytic approach. Clin Rehabil. 2014;28(7):704–16.
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