최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Therapeutic science for rehabilitation = 재활치료과학, v.11 no.4, 2022년, pp.23 - 39
배수영 (연세대학교 일반대학원 작업치료학과) , , 남상훈 (연세대학교 일반대학원 작업치료학과) , 홍익표 (연세대학교 소프트웨어디지털헬스케어융합대학 작업치료학과)
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 ...
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
Young, J., & Forster, A. (2007). Rehabilitation after stroke.?British Medical Journal, 334, 86-90. https://doi.org/10.1136/bmj.39059.456794.68?
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.