과제정보
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A3A2A 02096338). In addition, this research was supported in part by grant# K12 HD055929 from the National Institutes of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
참고문헌
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Ij, H. (2018). Statistics versus machine learning. Nature Methods, 15(4), 233-234. https://doi.org/10.1038/nmeth.4642
- 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
- 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
- Jakkula, V. (2006). Tutorial on support vector machine (svm). School of EECS, Washington State University. https://course.ccs.neu.edu/cs5100f11/resources/jakkula.pdf
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Meyer, D., & Wien, F. T. (2001). Support vector machines. R News, 1(3), 23-26.
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Young, J., & Forster, A. (2007). Rehabilitation after stroke. British Medical Journal, 334, 86-90. https://doi.org/10.1136/bmj.39059.456794.68