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Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke | Scientific Reports – Nature.com

Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke | Scientific Reports – Nature.com

  • Lin, X.-J., Lin, I. M. & Fan, S.-Y. Methodological issues in measuring health-related quality of life. Tzu Chi Med. J. 25, 8–12 (2013).

    Article  Google Scholar 

  • Guyatt, G. H. et al. Exploration of the value of health-related quality-of-life information from clinical research and into clinical practice. Mayo Clin. Proc. 82, 1229–1239 (2007).

    PubMed  Article  Google Scholar 

  • Virani, S. S. et al. Heart disease and stroke statistics 2021 update. Circulation 143, e254–e743 (2021).

    PubMed  Article  Google Scholar 

  • Carod-Artal, F. J. & Egido, J. A. Quality of life after stroke: The importance of a good recovery. Cerebrovasc. Dis. 27, 204–214 (2009).

    PubMed  Article  Google Scholar 

  • Nichols-Larsen, D. S., Clark, P. C., Zeringue, A., Greenspan, A. & Blanton, S. Factors influencing stroke survivors’ quality of life during subacute recovery. Stroke 36, 1480–1484 (2005).

    PubMed  Article  Google Scholar 

  • Bzdok, D. & Ioannidis, J. P. A. Exploration, inference, and prediction in neuroscience and biomedicine. Trends Neurosci. 42, 251–262 (2019).

    CAS  PubMed  Article  Google Scholar 

  • Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003).

    MATH  Google Scholar 

  • Heo, J. et al. Machine learning-based model for prediction of outcomes in acute stroke. Stroke 50, 1263–1265 (2019).

    PubMed  Article  Google Scholar 

  • Lin, W. Y. et al. Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation. Int. J. Med. Inform. 111, 159–164 (2018).

    PubMed  Article  Google Scholar 

  • Sale, P. et al. Predicting motor and cognitive improvement through machine learning algorithm in human subject that underwent a rehabilitation treatment in the early stage of stroke. J Stroke Cerebrovasc. Dis. 27, 2962–2972 (2018).

    PubMed  Article  Google Scholar 

  • Wang, H. L. et al. Automatic machine-learning-based outcome prediction in patients with primary intracerebral hemorrhage. Front. Neurol. 10, 910 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  • Thakkar, H. K., Liao, W. W., Wu, C. Y., Hsieh, Y. W. & Lee, T. H. Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches. J. Neuroeng. Rehabil. 17, 131 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  • Tokmakçı, M., Ünalan, D., Soyuer, F. & Öztürk, A. The reevaluate statistical results of quality of life in patients with cerebrovascular disease using adaptive network-based fuzzy inference system. Expert Syst. Appl. 34, 958–963 (2008).

    Article  Google Scholar 

  • Morris, J. H., van Wijck, F., Joice, S. & Donaghy, M. Predicting health related quality of life 6 months after stroke: The role of anxiety and upper limb dysfunction. Disabil. Rehabil. 35, 291–299 (2013).

    PubMed  Article  Google Scholar 

  • Sokolova, M. & Lapalme, G. A systematic analysis of performance measures for classification tasks. Inf Process Manag. 45, 427–437 (2009).

    Article  Google Scholar 

  • Turner, D. L., Ramos-Murguialday, A., Birbaumer, N., Hoffmann, U. & Luft, A. Neurophysiology of robot-mediated training and therapy: A perspective for future use in clinical populations. Front. Neurol. 4, 184 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  • Deconinck, F. J. et al. Reflections on mirror therapy: A systematic review of the effect of mirror visual feedback on the brain. Neurorehabil. Neural Repair 29, 349–361 (2015).

    PubMed  Article  Google Scholar 

  • Schlaug, G., Renga, V. & Nair, D. Transcranial direct current stimulation in stroke recovery. Arch. Neurol. 65, 1571–1576 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  • Kutner, N. G., Zhang, R., Butler, A. J., Wolf, S. L. & Alberts, J. L. Quality-of-life change associated with robotic-assisted therapy to improve hand motor function in patients with subacute stroke: A randomized clinical trial. Phys Ther. 90, 493–504 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  • Mehrholz, J. Is electromechanical and robot-assisted arm training effective for improving arm function in people who have had a stroke?: A cochrane review summary with commentary. Am. J. Phys. Med. Rehabil. 98, 339–340 (2019).

    PubMed  Article  Google Scholar 

  • Thieme, H. et al. Mirror therapy for improving motor function after stroke. Cochrane Database Syst. Rev. 7, Cd008449 (2018).

    PubMed  Google Scholar 

  • Bornheim, S. et al. Evaluating the effects of tDCS in stroke patients using functional outcomes: A systematic review. Disabil. Rehabil. 44, 13–23 (2022).

    PubMed  Article  Google Scholar 

  • Kang, N., Summers, J. J. & Cauraugh, J. H. Transcranial direct current stimulation facilitates motor learning post-stroke: A systematic review and meta-analysis. J. Neurol. Neurosurg. Psychiatry 87, 345 (2016).

    PubMed  Article  Google Scholar 

  • Liao, W. W. et al. Timing-dependent effects of transcranial direct current stimulation with mirror therapy on daily function and motor control in chronic stroke: A randomized controlled pilot study. J. Neuroeng. Rehabil. 17, 101 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  • An, T. G., Kim, S. H. & Kim, K. U. Effect of transcranial direct current stimulation of stroke patients on depression and quality of life. J. Phys. Ther. Sci. 29, 505–507 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  • Wu, C. Y., Huang, P. C., Chen, Y. T., Lin, K. C. & Yang, H. W. Effects of mirror therapy on motor and sensory recovery in chronic stroke: A randomized controlled trial. Arch. Phys. Med. Rehabil. 94, 1023–1030 (2013).

    PubMed  Article  Google Scholar 

  • Hsieh, Y. W. et al. Effects of home-based versus clinic-based rehabilitation combining mirror therapy and task-specific training for patients with stroke: A randomized crossover trial. Arch. Phys. Med. Rehabil. 99, 2399–2407 (2018).

    PubMed  Article  Google Scholar 

  • Hsieh, Y. W. et al. Comparison of proximal versus distal upper-limb robotic rehabilitation on motor performance after stroke: A cluster controlled trial. Sci. Rep. 8, 2091 (2018).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Woodbury, M. L., Velozo, C. A., Richards, L. G. & Duncan, P. W. Rasch analysis staging methodology to classify upper extremity movement impairment after stroke. Arch. Phys. Med. Rehabil. 94, 1527–1533 (2013).

    PubMed  Article  Google Scholar 

  • Gregson, J. M. et al. Reliability of the Tone Assessment Scale and the modified Ashworth scale as clinical tools for assessing poststroke spasticity. Arch. Phys. Med. Rehabil. 80, 1013–1016 (1999).

    CAS  PubMed  Article  Google Scholar 

  • Rossi, S. et al. Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: Expert guidelines. Clin. Neurophysiol. 132, 269–306 (2021).

    PubMed  Article  Google Scholar 

  • Duncan, P. W., Bode, R. K., Min Lai, S. & Perera, S. Rasch analysis of a new stroke-specific outcome scale: The Stroke Impact Scale. Arch. Phys. Med. Rehabil. 84, 950–963 (2003).

    PubMed  Article  Google Scholar 

  • Carod-Artal, F. J., Coral, L. F., Trizotto, D. S. & Moreira, C. M. The Stroke Impact Scale 3.0. Stroke 39, 2477–2484 (2008).

    PubMed  Article  Google Scholar 

  • Lin, K. C. et al. Psychometric comparisons of the Stroke Impact Scale 3.0 and stroke-specific quality of life scale. Qual. Life Res. 19, 435–443 (2010).

    PubMed  Article  Google Scholar 

  • Richardson, M., Campbell, N., Allen, L., Meyer, M. & Teasell, R. The stroke impact scale: Performance as a quality of life measure in a community-based stroke rehabilitation setting. Disabil. Rehabil. 38, 1425–1430 (2016).

    PubMed  Article  Google Scholar 

  • Duncan, P. W. et al. The stroke impact scale version 2.0. Evaluation of reliability, validity, and sensitivity to change. Stroke 30, 2131–2140 (1999).

    CAS  PubMed  Article  Google Scholar 

  • Lang, C. E., Edwards, D. F., Birkenmeier, R. L. & Dromerick, A. W. Estimating minimal clinically important differences of upper-extremity measures early after stroke. Arch. Phys. Med. Rehabil. 89, 1693–1700 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  • van der Lee, J. H. et al. Forced use of the upper extremity in chronic stroke patients: Results from a single-blind randomized clinical trial. Stroke 30, 2369–2375 (1999).

    PubMed  Article  Google Scholar 

  • Hägg, O., Fritzell, P. & Nordwall, A. The clinical importance of changes in outcome scores after treatment for chronic low back pain. Eur. Spine J. 12, 12–20 (2003).

    PubMed  Article  Google Scholar 

  • Wu, C. Y., Chuang, L. L., Lin, K. C., Lee, S. D. & Hong, W. H. Responsiveness, minimal detectable change, and minimal clinically important difference of the Nottingham Extended Activities of Daily Living Scale in patients with improved performance after stroke rehabilitation. Arch. Phys. Med. Rehabil. 92, 1281–1287 (2011).

    PubMed  Article  Google Scholar 

  • Lemmens, R. J., Timmermans, A. A., Janssen-Potten, Y. J., Smeets, R. J. & Seelen, H. A. Valid and reliable instruments for arm-hand assessment at ICF activity level in persons with hemiplegia: A systematic review. BMC Neurol. 12, 21 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  • Chen, C. M. et al. Potential predictors for health-related quality of life in stroke patients undergoing inpatient rehabilitation. Health Qual. Life Outcomes 13, 118 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  • Coupar, F., Pollock, A., Rowe, P., Weir, C. & Langhorne, P. Predictors of upper limb recovery after stroke: A systematic review and meta-analysis. Clin. Rehabil. 26, 291–313 (2012).

    PubMed  Article  Google Scholar 

  • Chiti, G. & Pantoni, L. Use of Montreal Cognitive Assessment in patients with stroke. Stroke 45, 3135–3140 (2014).

    PubMed  Article  Google Scholar 

  • Fugl-Meyer, A. R., Jaasko, 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).

    CAS  PubMed  Google Scholar 

  • Wolf, S. L. et al. Assessing Wolf motor function test as outcome measure for research in patients after stroke. Stroke 32, 1635–1639 (2001).

    CAS  PubMed  Article  Google Scholar 

  • Gregson, J. M. et al. Reliability of measurements of muscle tone and muscle power in stroke patients. Age Ageing 29, 223–228 (2000).

    CAS  PubMed  Article  Google Scholar 

  • van der Lee, J. H., Beckerman, H., Knol, D. L., de Vet, H. C. & Bouter, L. M. Clinimetric properties of the motor activity log for the assessment of arm use in hemiparetic patients. Stroke 35, 1410–1414 (2004).

    ADS  PubMed  Article  Google Scholar 

  • Desrosiers, J., Bravo, G., Hébert, R., Dutil, É. & Mercier, L. Validation of the Box and Block Test as a measure of dexterity of elderly people: Reliability, validity, and norms studies. Arch. Phys. Med. Rehabil. 75, 751–755 (1994).

    CAS  PubMed  Article  Google Scholar 

  • Wu, C. Y., Chuang, I. C., Ma, H. I., Lin, K. C. & Chen, C. L. Validity and responsiveness of the Revised Nottingham Sensation Assessment for outcome evaluation in stroke rehabilitation. Am. J. Occup. Ther. 70, 1–8 (2016).

    Google Scholar 

  • Linacre, J. M., Heinemann, A. W., Wright, B. D., Granger, C. V. & Hamilton, B. B. The structure and stability of the functional independence measure. Arch. Phys. Med. Rehabil. 75, 127–132 (1994).

    CAS  PubMed  Article  Google Scholar 

  • Sarker, S. J., Rudd, A. G., Douiri, A. & Wolfe, C. D. Comparison of 2 extended activities of daily living scales with the Barthel Index and predictors of their outcomes: Cohort study within the South London Stroke Register (SLSR). Stroke 43, 1362–1369 (2012).

    PubMed  Article  Google Scholar 

  • Tin, K. H. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998).

    Article  Google Scholar 

  • Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967).

    MATH  Article  Google Scholar 

  • Zhu, M., Chen, W., Hirdes, J. P. & Stolee, P. The K-nearest neighbor algorithm predicted rehabilitation potential better than current clinical assessment protocol. J. Clin. Epidemiol. 60, 1015–1021 (2007).

    PubMed  Article  Google Scholar 

  • Manning, T., Sleator, R. D. & Walsh, P. Biologically inspired intelligent decision making. Bioengineered 5, 80–95 (2014).

    PubMed  Article  Google Scholar 

  • Abedi, V. et al. Novel screening tool for stroke using artificial neural network. Stroke 48, 1678–1681 (2017).

    PubMed  Article  Google Scholar 

  • Smola, A. J. & Schölkopf, B. A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004).

    MathSciNet  Article  Google Scholar 

  • Kim, J. K., Choo, Y. J. & Chang, M. C. Prediction of motor function in stroke patients using machine learning algorithm: Development of practical models. J. Stroke Cerebrovasc. Dis. 30, 105856 (2021).

    PubMed  Article  Google Scholar 

  • Jiawei, H. M. K. & Jian, P. Data Mining: Concepts and Techniques 2nd edn. (Morgan Kaufmann, 2006).

    MATH  Google Scholar 

  • Shouman, M., Turner, T. & Stocker, R. Using decision tree for diagnosing heart disease patients. In Proceedings of the Ninth Australasian Data Mining Conference, vol. 121, 23–30 (Australian Computer Society, Inc., 2011).

  • Kent, J. T. Information gain and a general measure of correlation. Biometrika 70, 163–173 (1983).

    MathSciNet  MATH  Article  Google Scholar 

  • Wang, W. et al. A systematic review of machine learning models for predicting outcomes of stroke with structured data. PLoS ONE 15, e0234722 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Luo, W. et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: A multidisciplinary view. J. Med. Internet Res. 18, e323 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  • Rodriguez, J. D., Perez, A. & Lozano, J. A. Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 569–575 (2010).

    PubMed  Article  Google Scholar 

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    MATH  Article  Google Scholar 

  • Hall, M. et al. The WEKA data mining software: An update. SIGKDD Explor. Newsl. 11, 10–18 (2009).

    Article  Google Scholar 

  • Pandey, A. K., Rajpoot, D. S. & Rajpoot, D. S. A comparative study of classification techniques by utilizing WEKA. In 2016 International Conference on Signal Processing and Communication (ICSC) (2016).

  • Frank, E., Hall, M., Trigg, L., Holmes, G. & Witten, I. H. Data mining in bioinformatics using Weka. Bioinformatics 20, 2479–2481 (2004).

    CAS  PubMed  Article  Google Scholar 

  • Sim, J. A. et al. The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: Applications of machine learning. Sci. Rep. 10, 10693 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Scrutinio, D. et al. Machine learning to predict mortality after rehabilitation among patients with severe stroke. Sci. Rep. 10, 20127 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Apao, N. J., Feliscuzo, L. S., Romana, C. L. S. & Tagaro, J. Multiclass classification using random forest algorithm to prognosticate the level of activity of patients with stroke. IJSTR 9, 1233–1240 (2020).

    Google Scholar 

  • Badriyah, T., Sakinah, N., Syarif, I. & Syarif, D. R. Machine learning algorithm for stroke disease classification. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (2020).

  • White, J. et al. Predictors of health-related quality of life in community-dwelling stroke survivors: A cohort study. Fam. Pract. 33, 382–387 (2016).

    PubMed  Article  Google Scholar 

  • Katona, M., Schmidt, R., Schupp, W. & Graessel, E. Predictors of health-related quality of life in stroke patients after neurological inpatient rehabilitation: A prospective study. Health Qual. Life Outcomes 13, 58 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  • Huang, P. C. et al. Predictors of motor, daily function, and quality-of-life improvements after upper-extremity robot-assisted rehabilitation in stroke. Am. J. Occup. Ther. 68, 325–333 (2014).

    PubMed  Article  Google Scholar 

  • Nijenhuis, S. M. et al. Strong relations of elbow excursion and grip strength with post-stroke arm function and activities: Should we aim for this in technology-supported training?. J. Rehabil. Assist. Technol. Eng. 5, 2055668318779301 (2018).

    PubMed  PubMed Central  Google Scholar 

  • Clarke, P. & Black, S. E. Quality of life following stroke: Negotiating disability, identity, and resources. J. Appl. Gerontol. 24, 319–336 (2005).

    Article  Google Scholar 

  • Pedersen, S. G. et al. Stroke-specific quality of life one-year post-stroke in two Scandinavian country-regions with different organisation of rehabilitation services: A prospective study. Disabil. Rehabil. 43, 3810–3820 (2021).

    PubMed  Article  Google Scholar 

  • Doyle, S., Bennett, S., Fasoli, S. E. & McKenna, K. T. Interventions for sensory impairment in the upper limb after stroke. Cochrane Database Syst. Rev. 6, CD006331 (2010).

    Google Scholar 

  • Wu, C. W., Seo, H. J. & Cohen, L. G. Influence of electric somatosensory stimulation on paretic-hand function in chronic stroke. Arch. Phys. Med. Rehabil. 87, 351–357 (2006).

    PubMed  Article  Google Scholar 

  • Turville, M., Carey, L. M., Matyas, T. A. & Blennerhassett, J. Change in functional arm use is associated with somatosensory skills after sensory retraining poststroke. Am. J. Occup. Ther. 71, 1–9 (2017).

    Article  Google Scholar 

  • Meyer, S., Karttunen, A. H., Thijs, V., Feys, H. & Verheyden, G. How do somatosensory deficits in the arm and hand relate to upper limb impairment, activity, and participation problems after stroke? A systematic review. Phys. Ther. 94, 1220–1231 (2014).

    PubMed  Article  Google Scholar 

  • Rokach, L. Ensemble methods for classifiers. In Data Mining and Knowledge Discovery Handbook (eds Maimon, O. & Rokach, L.) 957–980 (Springer, 2005).

    MATH  Chapter  Google Scholar 

  • Hung, C. Y., Chen, W. C., Lai, P. T., Lin, C. H. & Lee, C. C. Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2017, 3110–3113 (2017).

    Google Scholar 

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