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http://dx.doi.org/10.22683/tsnr.2022.11.4.023

Predicting Functional Outcomes of Patients With Stroke Using Machine Learning: A Systematic Review  

Bae, Suyeong (Dept. of Occupational Therapy, Graduate School, Yonsei University)
Lee, Mi Jung (Dept. of Nutrition, Metabolism and Rehabilitation Sciences, School of Health Professions, University of Texas Medical Branch at Galveston)
Nam, Sanghun (Dept. of Occupational Therapy, Graduate School, Yonsei University)
Hong, Ickpyo (Dept. of Occupational Therapy, College of Software and Digital Healthcare Convergence, Yonsei University)
Publication Information
Therapeutic Science for Rehabilitation / v.11, no.4, 2022 , pp. 23-39 More about this Journal
Abstract
Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.
Keywords
Machine learning; Occupational therapy; Physical therapy; Recovery of function; Rehabilitation research; Stroke;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. Stata Journal, 20(1), 3-29. https://doi.org/10.1177/1536867X20909688   DOI
2 Scrutinio, D., Ricciardi, C., Donisi, L., Losavio, E., Battista, P., Guida, P., Cesarelli, M., Pagano, G., & D'Addio, G. (2020). Machine learning to predict mortality after rehabilitation among patients with severe stroke. Scientific Reports, 10(1), 1-10. https://doi.org/10.1038/s41598-020-77243-3   DOI
3 Siegert, R. J., & Taylor, W. J. (2004). Theoretical aspects of goal-setting and motivation in rehabilitation. Disability and Rehabilitation, 26(1), 1-8. https://doi.org/10.1080/09638280410001644932   DOI
4 Singh, A., Thakur, N., & Sharma, A. (2016). A review of supervised machine learning algorithms. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, 1310-1315.
5 Sirsat, M. S., Ferme, E., & Camara, J. (2020). Machine learning for brain stroke: A review. Journal of Stroke and Cerebrovascular Diseases, 29(10), 1-17. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105162   DOI
6 Son, Y. J., Kim, H. G., Kim, E. H., Choi, S., & Lee, S. K. (2010). Application of support vector machine for prediction of medication adherence in heart failure patients. Healthcare Informatics Research, 16(4), 253-259. https://doi.org/10.4258/hir.2010.16.4.253   DOI
7 Stylianou, N., Akbarov, A., Kontopantelis, E., Buchan, I., & Dunn, K. W. (2015). Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches. Burns, 41(5), 925-934. https://doi.org/10.1016/j.burns.2015.03.016   DOI
8 Suzuki, M., Sugimura, S., Suzuki, T., Sasaki, S., Abe, N., Tokito, T., & Hamaguchi, T. (2020). Machine-learning prediction of self-care activity by grip strengths of both hands in poststroke hemiplegia. Medicine, 99(11). http://doi.org/10.1097/MD.0000000000019512   DOI
9 Tozlu, C., Edwards, D., Boes, A., Labar, D., Tsagaris, K. Z., Silverstein, J., Lane, H. P., Subuncu, M. R., Liu, C., & Kuceyeski, A. (2020). Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke. Neurorehabilitation and Neural Repair, 34(5), 428-439. https://doi.org/10.1177/1545968320909796   DOI
10 Wang, W., Kiik, M., Peek, N., Curcin, V., Marshall, I. J., Rudd, A. G., Wang, Y., Douiri, A., Wolfe, C. D., & Bray, B. (2020). A systematic review of machine learning models for predicting outcomes of stroke with structured data. PLoS One, 15(6), 1-16. https://doi.org/10.1371/journal.pone.0234722   DOI
11 Ward, N. S. (2017). Restoring brain function after stroke-bridging the gap between animals and humans. Nature Reviews Neurology, 13(4), 244-255. https://doi.org/10.1038/nrneurol.2017.34   DOI
12 Woo, Y. C., Lee, S. Y., Choi, W., Ahn, C. W., & Baek, O. K. (2019). Trend of utilization of machine learning technology for digital healthcare data analysis. Electronics and Telecommunications Trends, 34(1), 98-110. https://doi.org/10.22648/ETRI.2019.J.340109   DOI
13 Young, J., & Forster, A. (2007). Rehabilitation after stroke. British Medical Journal, 334, 86-90. https://doi.org/10.1136/bmj.39059.456794.68    DOI
14 Al-Qazzaz, N. K., Ali, S. H., Ahmad, S. A., Islam, S., & Mohamad, K. (2014). Cognitive impairment and memory dysfunction after a stroke diagnosis: A post-stroke memory assessment. Neuropsychiatric Dsease and Treatment, 10, 1677-1691. http://doi.org/10.2147/NDT.S67184   DOI
15 Caro, C. C., Costa, J. D., & da Cruz, D. M. C. (2018). Burden and quality of life of family caregivers of stroke patients. Occupational Therapy in Health Care, 32(2), 154-171. https://doi.org/10.1080/07380577.2018.1449046   DOI
16 Alaka, S. A., Menon, B. K., Brobbey, A., Williamson, T., Goyal, M., Demchuk, A. M., Hill, M. D., & Sajobi, T. T. (2020). Functional outcome prediction in ischemic stroke: A comparison of machine learning algorithms and regression models. Frontiers in Neurology, 11. https://doi.org/10.3389/fneur.2020.00889   DOI
17 American Occupational Therapy Association. (2020). Occupational therapy practice framework: Domain and process. American Journal of Occupational Therapy, 74(S2), 1-85. https://doi.org/10.5014/ajot.2020.74S2001   DOI
18 Byeon, H. (2020). Is the Random Forest algorithm suitable for predicting Parkinson's disease with mild cognitive impairment out of Parkinson's disease with normal cognition? International Journal of Environmental Research and Public Health, 17(7), 2594-2608. https://doi.org/10.3390/ijerph17072594   DOI
19 Cheong, M. J., Jeon, B., & Noh, S. E. (2020). A protocol for systematic review and meta-analysis on psychosocial factors related to rehabilitation motivation of stroke patients. Medicine, 99(52), e23727-e23727. http://doi.org/10.1097/MD.0000000000023727   DOI
20 Clarke, D. J., & Forster, A. (2015). Improving post-stroke recovery: The role of the multidisciplinary health care team. Journal of Multidisciplinary Healthcare, 8, 433-442. http://doi.org/10.2147/JMDH.S68764   DOI
21 Dworzynski, K., Ritchie, G., & Playford, E. D. (2015). Stroke rehabilitation: Long-term rehabilitation after stroke. Clinical Medicine, 15(5), 461-464. http://doi.org/10.7861/clinmedicine.15-5-461   DOI
22 Jakkula, V. (2006). Tutorial on support vector machine (svm). School of EECS, Washington State University. https://course.ccs.neu.edu/cs5100f11/resources/jakkula.pdf
23 Elloker, T., Rhoda, A., Arowoiya, A., & Lawal, I. U. (2019). Factors predicting community participation in patients living with stroke, in the Western Cape, South Africa. Disability and Rehabilitation, 41(22), 2640-2647. https://doi.org/10.1080/09638288.2018.1473509   DOI
24 Fishman, K. N., Ashbaugh, A. R., & Swartz, R. H. (2021). Goal setting improves cognitive performance in a randomized trial of chronic stroke survivors. Stroke, 52(2), 458-470. https://doi.org/10.1161/STROKEAHA.120.032131   DOI
25 Harari, Y., O'Brien, M. K., Lieber, R. L., & Jayaraman, A. (2020). Inpatient stroke rehabilitation: Prediction of clinical outcomes using a machine-learning approach. Journal of NeuroEngineering and Rehabilitation, 17(1), 1-10. https://doi.org/10.1186/s12984-020-00704-3   DOI
26 Heo, J., Yoon, J. G., Park, H., Kim, Y. D., Nam, H. S., & Heo, J. H. (2019). Machine learning-based model for prediction of outcomes in acute stroke. Stroke, 50(5), 1263-1265. https://doi.org/10.1161/STROKEAHA.118.024293   DOI
27 Ij, H. (2018). Statistics versus machine learning. Nature Methods, 15(4), 233-234. https://doi.org/10.1038/nmeth.4642   DOI
28 Iwamoto, Y., Imura, T., Tanaka, R., Imada, N., Inagawa, T., Araki, H., & Araki, O. (2020). Development and validation of machine learning-based prediction for dependence in the activities of daily living after stroke inpatient rehabilitation: A decision-tree analysis. Journal of Stroke and Cerebrovascular Diseases, 29(12), 1-6. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105332   DOI
29 Imura, T., Inoue, Y., Tanaka, R., Matsuba, J., & Umayahara, Y. (2021). Clinical features for identifying the possibility of toileting independence after convalescent inpatient rehabilitation in severe stroke patients: A decision tree analysis based on a nationwide Japan rehabilitation database. Journal of Stroke and Cerebrovascular Diseases, 30(2), 1-8. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105483   DOI
30 Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. http://doi.org/10.1126/science.aaa8415   DOI
31 Korpershoek, C., van der Bijl, J., & Hafsteinsdottir, T. B. (2011). Self-efficacy and its influence on recovery of patients with stroke: A systematic review. Journal of Advanced Nursing, 67(9), 1876-1894. https://doi.org/10.1111/j.1365-2648.2011.05659.x   DOI
32 Maso, I., Pinto, E. B., Monteiro, M., Makhoul, M., Mendel, T., Jesus, P. A., & Oliveira-Filho, J. (2019). A simple hospital mobility scale for acute ischemic stroke patients predicts long-term functional outcome. Neurorehabilitation and Neural Repair, 33(8), 614-622. https://doi.org/10.1177/1545968319856894   DOI
33 Liao, W. W., Hsieh, Y. W., Lee, T. H., Chen, C. L., & Wu, C. Y. (2022). Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke. Scientific Reports, 12(1), 1-10. https://doi.org/10.1038/s41598-022-14986-1   DOI
34 Lin, W., Chen, C., Tseng, Y. J., Tsai, Y. T., Chang, C. Y., Wang, H. Y., & Chen, C. K. (2018). Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation. International Journal of Medical Informatics, 111, 159-164. https://doi.org/10.1016/j.ijmedinf.2018.01.002   DOI
35 Lin, C., Hsu, K., Johnson, K. R., Fann, Y. C., Tsai, C., Sun, Y., Lien, L., Chang, W., Chen, P., Lin, C., & Hsu, C. Y. (2020). Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry. Computer Methods and Programs in Biomedicine, 190, 1-14. https://doi.org/10.1016/j.cmpb.2020.105381   DOI
36 Mercier, L., Audet, T., Hebert, R., Rochette, A., & Dubois, M. F. (2001). Impact of motor, cognitive, and perceptual disorders on ability to perform activities of daily living after stroke. Stroke, 32(11), 2602-2608. https://doi.org/10.1161/hs1101.098154   DOI
37 Meyer, D., & Wien, F. T. (2001). Support vector machines. R News, 1(3), 23-26.
38 Platz, T. (2019). Evidence-based guidelines and clinical pathways in stroke rehabilitation-an international perspective. Frontiers in Neurology, 10, 1-7. https://doi.org/10.3389/fneur.2019.00200   DOI