ArticleScientific reports2019
Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Trial
- Artificial Intelligence Applications to Support Physical Activity, Mobility, and Fatigue Management in People with Multiple Sclerosis: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Assessment of Fall Risk in Neurological Disorders and Technology: Relationship Between Silver Index and Gait Analysis.Sensors (Basel, Switzerland) · 2026Article
- Increased visual reliance for postural control in multiple sclerosis: preliminary evidence from task-related cortical desynchronization.Frontiers in neurology · 2026Article
- Investigating Performance of an Embedded Machine Learning Solution for Classifying Postural Behaviors.Sensors (Basel, Switzerland) · 2025Article
- Ranking the difficulty of the cognitive tasks in Dual-Tasks during walking in healthy adults.Neurosciences (Riyadh, Saudi Arabia) · 2025Article
- Postural sway in multiple sclerosis patients: interaction of vision, surface, and fatigue effects.Frontiers in human neuroscience · 2025Article
- Linking Pathogenesis to Fall Risk in Multiple Sclerosis.Archives of internal medicine research · 2025Article
- The effects of type 2 diabetes mellitus on postural adaptation and complexity during a visual search task in older adults.Frontiers in aging neuroscience · 2025Article
- Walk Longer! Using Wearable Inertial Sensors to Uncover Which Gait Aspects Should Be Treated to Increase Walking Endurance in People with Multiple Sclerosis.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence and Multiple Sclerosis.Current neurology and neuroscience reports · 2024Review
- A machine learning approach to determine the risk factors for fall in multiple sclerosis.BMC medical informatics and decision making · 2024Article
- Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach.Journal of neuroengineering and rehabilitation · 2024Article
- Sample Entropy Improves Assessment of Postural Control in Early-Stage Multiple Sclerosis.Sensors (Basel, Switzerland) · 2024Article
- Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking.Sensors (Basel, Switzerland) · 2024Article
- Machine learning's effectiveness in evaluating movement in one-legged standing test for predicting high autistic trait.Frontiers in psychiatry · 2024Article
- Assessing Free-Living Postural Sway in Persons With Multiple Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- Uncovering Subtle Gait Deterioration in People with Early-Stage Multiple Sclerosis Using Inertial Sensors: A 2-Year Multicenter Longitudinal Study.Sensors (Basel, Switzerland) · 2023Article
- Fall Assessment and Monitoring in People With Multiple Sclerosis: A Practical Evidence-Based Review for Clinicians.Neurology. Clinical practice · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Numerous postural sway metrics have been shown to be sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation implemented a machine learning approach to assess the accuracy and feature importance of various postural sway metrics to differentiate individuals with MS from healthy controls as a function of physiological fall risk. 153 participants (50 controls and 103 individuals with MS) underwent a static posturography assessment and a physiological fall risk assessment. Participants were further classified into four subgroups based on fall risk: controls, low-risk MS (n = 34), moderate-risk MS (n = 27), high-risk MS (n = 42). Twenty common sway metrics were derived following standard procedures and subsequently used to train a machine learning algorithm (random forest - RF, with 10-fold cross validation) to predict individuals' fall risk grouping. The sway-metric based RF classifier had high accuracy in discriminating controls from MS individuals (>86%). Sway sample entropy was identified as the strongest feature for classification of low-risk MS individuals from healthy controls. Whereas for all other comparisons, mediolateral sway amplitude was identified as the strongest predictor for fall risk groupings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.