World Health Organization. International Classification of Functioning, Disability and Health (ICF): Mobility. (2023). https://icd.who.int/dev11/l-icf/en#/http://id.who.int/icd/entity/2048203604. Accessed 10 Apr 2024.Coleman, C. I., Sidovar, M. F., Roberts, M. S. & Kohn, C. Impact of mobility impairment on indirect costs and health-related quality of life in multiple sclerosis. PLoS One 8, e54756 (2013).ADSÂ
CASÂ
PubMedÂ
Google ScholarÂ
Kim, H. J. et al. The Economic Burden of Brain disability in Korea, 2008–2011. Inquiry 57, 46958020936396 (2020).PubMedÂ
Google ScholarÂ
Khan, F. et al. Prediction of factors affecting mobility in patients with stroke and finding the Mediation Effect of Balance on mobility: a cross-sectional study. Int. J. Environ. Res. Public. Health 19, 16612 (2022).PubMedÂ
Google ScholarÂ
Shao, C., Wang, Y., Gou, H. & Chen, T. The factors associated with the deterioration of activities of daily life in stroke patients: a retrospective cohort study. Top. Stroke Rehabil 31, 21–28 (2024).PubMedÂ
Google ScholarÂ
Fonseca, E. P. D. et al. Balance, functional mobility, and fall occurrence in patients with human T-cell lymphotropic virus type-1-associated myelopathy/tropical spastic paraparesis: a cross-sectional study. Rev. Soc. Bras. Med. Trop. 51, 162–167 (2018).PubMedÂ
Google ScholarÂ
Prata, M. G. & Scheicher, M. E. Correlation between balance and the level of functional independence among elderly people. Sao Paulo Med. J. 130, 97–101 (2012).PubMedÂ
Google ScholarÂ
Berg, K. Measuring Balance in the Elderly: Development and Validation of an Instrument (McGill University, 1992).Lee, S., Na, Y., Tae, W. S. & Pyun, S. B. Clinical and neuroimaging factors associated with aphasia severity in stroke patients: diffusion tensor imaging study. Sci. Rep. 10, 12874 (2020).ADSÂ
CASÂ
PubMedÂ
Google ScholarÂ
Jiang, H., van Zijl, P. C., Kim, J., Pearlson, G. D. & Mori, S. DtiStudio: resource program for diffusion tensor computation and fibre bundle tracking. Comput. Methods Programs Biomed. 81, 106–116 (2006).PubMedÂ
Google ScholarÂ
Amiri, M. et al. Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study. Brain 146, 50–64 (2023).PubMedÂ
Google ScholarÂ
Dewenter, A. et al. Disentangling the effects of Alzheimer’s and small vessel disease on white matter fibre tracts. Brain 146, 678–689 (2023).PubMedÂ
Google ScholarÂ
Mayer, A. R. et al. Multicompartmental models and diffusion abnormalities in paediatric mild traumatic brain injury. Brain 145, 4124–4137 (2022).PubMedÂ
Google ScholarÂ
Katz, N. K. et al. Predictors of functional outcomes in patients with facioscapulohumeral muscular dystrophy. Brain 144, 3451–3460 (2021).PubMedÂ
Google ScholarÂ
Landrigan, J. F., Zhang, F. & Mirman, D. A data-driven approach to post-stroke aphasia classification and lesion-based prediction. Brain 144, 1372–1383 (2021).PubMedÂ
Google ScholarÂ
North, R. Y. et al. Electrophysiological and transcriptomic correlates of neuropathic pain in human dorsal root ganglion neurons. Brain 142, 1215–1226 (2019).PubMedÂ
Google ScholarÂ
He, X. et al. Disrupted dynamic network reconfiguration of the language system in temporal lobe epilepsy. Brain 141, 1375–1389 (2018).PubMedÂ
Google ScholarÂ
Kim, R., Kim, C. W., Park, H. & Lee, K. S. Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition. Sci. Rep. 13, 11651 (2023).ADSÂ
CASÂ
PubMedÂ
Google ScholarÂ
Cho, H., Lee, E. H., Lee, K. S. & Heo, J. S. Machine learning-based risk factor analysis of necrotizing enterocolitis in very low birth weight infants. Sci. Rep. 12, 21407 (2022).ADSÂ
CASÂ
PubMedÂ
Google ScholarÂ
Cho, H., Lee, E. H., Lee, K. S. & Heo, J. S. Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants. Sci. Rep. 12, 12119 (2022).ADSÂ
CASÂ
PubMedÂ
Google ScholarÂ
Lee, K. S. & Kim, E. S. Explainable Artificial Intelligence in the early diagnosis of gastrointestinal disease. Diagnostics (Basel) 12, 2740 (2022).PubMedÂ
Google ScholarÂ
Michael, K. M., Allen, J. K. & Macko, R. F. Reduced ambulatory activity after stroke: the role of balance, gait, and cardiovascular fitness. Arch. Phys. Med. Rehabil 86, 1552–1556 (2005).PubMedÂ
Google ScholarÂ
Winter, D. A. Human balance and posture control during standing and walking. Gait Posture 3, 193–214 (1995).
Google ScholarÂ
Gath, C. F., Gianella, M. G., Bonamico, L., Olmos, L. & Russo, M. J. Prediction of Balance after Inpatient Rehabilitation in stroke subjects with severe balance alterations at the admission. J. Stroke Cerebrovasc. Dis. 30, 105627 (2021).PubMedÂ
Google ScholarÂ
Lima, C. A., Ricci, N. A., Nogueira, E. C. & Perracini, M. R. The Berg Balance Scale as a clinical screening tool to predict fall risk in older adults: a systematic review. Physiotherapy 104, 383–394 (2018).CASÂ
PubMedÂ
Google ScholarÂ
Maeda, N., Kato, J. & Shimada, T. Predicting the probability for fall incidence in stroke patients using the Berg Balance Scale. J. Int. Med. Res. 37, 697–704 (2009).CASÂ
PubMedÂ
Google ScholarÂ
Mackintosh, S. F., Hill, K. D., Dodd, K. J., Goldie, P. A. & Culham, E. G. Balance score and a history of falls in hospital predict recurrent falls in the 6 months following stroke rehabilitation. Arch. Phys. Med. Rehabil 87, 1583–1589 (2006).PubMedÂ
Google ScholarÂ
Fugl-Meyer, A. R., Jääskö, L., Leyman, I., Olsson, S. & Steglind, S. The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand. J. Rehabil Med. 7, 13–31 (1975).CASÂ
PubMedÂ
Google ScholarÂ
Kollen, B., van de Port, I., Lindeman, E., Twisk, J. & Kwakkel, G. Predicting improvement in gait after stroke: a longitudinal prospective study. Stroke 36, 2676–2680 (2005).PubMedÂ
Google ScholarÂ
Chou, C. Y. et al. Developing a short form of the Berg Balance Scale for people with stroke. Phys. Ther. 86, 195–204 (2006).ADSÂ
PubMedÂ
Google ScholarÂ
Lee, E. Y. et al. Short-term and long-term predictors of balance function in stroke patients: a 6-month follow-up study. Int. J. Rehabil Res. 46, 163–169 (2023).PubMedÂ
Google ScholarÂ
Smith, M. C., Barber, P. A. & Stinear, C. M. The TWIST Algorithm Predicts Time to walking independently after stroke. Neurorehabil Neural Repair. 31, 955–964 (2017).PubMedÂ
Google ScholarÂ
Ahn, Y. H., Ahn, S. H., Kim, H., Hong, J. H. & Jang, S. H. Can stroke patients walk after complete lateral corticospinal tract injury of the affected hemisphere? Neuroreport 17, 987–990 (2006).PubMedÂ
Google ScholarÂ
Dawes, H. et al. Walking performance and its recovery in chronic stroke in relation to extent of lesion overlap with the descending motor tract. Exp. Brain Res. 186, 325–333 (2008).CASÂ
PubMedÂ
Google ScholarÂ
Soyuer, F. & Oztürk, A. The effect of spasticity, sense and walking aids in falls of people after chronic stroke. Disabil. Rehabil 29, 679–687 (2007).PubMedÂ
Google ScholarÂ
Lubetzky-Vilnai, A. & Kartin, D. The effect of balance training on balance performance in individuals poststroke: a systematic review. J. Neurol. Phys. Ther. 34, 127–137 (2010).PubMedÂ
Google ScholarÂ
Kwakkel, G. & Kollen, B. J. Predicting activities after stroke: what is clinically relevant? Int. J. Stroke 8, 25–32 (2013).CASÂ
PubMedÂ
Google ScholarÂ
Bagg, S., Pombo, A. P. & Hopman, W. Effect of age on functional outcomes after stroke rehabilitation. Stroke 33, 179–185 (2002).PubMedÂ
Google ScholarÂ
Burke, S. N. & Barnes, C. A. Neural plasticity in the ageing brain. Nat. Rev. Neurosci. 7, 30–40 (2006).CASÂ
PubMedÂ
Google ScholarÂ
Gheno, R., Cepparo, J. M., Rosca, C. E. & Cotten, A. Musculoskeletal disorders in the elderly. J. Clin. Imaging Sci. 2, 39 (2012).PubMedÂ
Google ScholarÂ
Kwah, L. K. & Diong, J. National Institutes of Health Stroke Scale (NIHSS). J. Physiother 60, 61 (2014).PubMedÂ
Google ScholarÂ
Couronné, R., Probst, P. & Boulesteix, A. L. Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinform. 19, 270 (2018).
Google ScholarÂ
Lundberg, S. M., Erion, G. G. & Lee, S. I. Consistent individualized feature attribution for tree ensembles. ArXiv. 2019;1802.03888.