Predicting falls and injuries in people with multiple sclerosis using machine learning algorithms
Author:
Publisher
Elsevier BV
Subject
Clinical Neurology,Neurology,General Medicine
Reference19 articles.
1. Falls among adults aging with disability;Matsuda;Arch. Phys. Med. Rehabil.,2015
2. Evaluation of a web-based fall prevention program among people with multiple sclerosis;Kannan;Mult. Scler. Relat. Disord/,2019
3. Level of mobility limitations and falls status in persons with multiple sclerosis;Coote;Arch. Phys. Med. Rehabil.,2014
4. Neuropsychological, balance, and mobility risk factors for falls in people with multiple sclerosis: a prospective cohort study;Hoang;Arch. Phys. Med. Rehabil.,2014
5. Frequency, characteristics, and consequences of falls in multiple sclerosis: findings from a cohort study;Gunn;Arch. Phys. Med. Rehabil.,2014
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1. Enhancing trustworthiness and reliability: advance explainable artificial intelligence framework for real world Sclerosis detection;Physica Scripta;2024-09-04
2. A machine learning approach to determine the risk factors for fall in multiple sclerosis;BMC Medical Informatics and Decision Making;2024-07-30
3. Fall Assessment and Monitoring in People With Multiple Sclerosis;Neurology Clinical Practice;2023-10
4. Detection of Fall Risk in Multiple Sclerosis by Gait Analysis—An Innovative Approach Using Feature Selection Ensemble and Machine Learning Algorithms;Brain Sciences;2022-10-31
5. Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach;Journal of Clinical Medicine;2022-06-17
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