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International journal of medical informatics v.111, 2018년, pp.159 - 164   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation

Lin, Wan-Yin (Department of Physical Medicine & Rehabilitation, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan ) ; Chen, Chun-Hsien (Department of Information Management, Chang Gung University, Taoyuan City, Taiwan ) ; Tseng, Yi-Ju (Department of Information Management, Chang Gung University, Taoyuan City, Taiwan ) ; Tsai, Yu-Ting (School of Medicine, Chang Gung University, Taoyuan City, Taiwan ) ; Chang, Ching-Yu (School of Medicine, Chang Gung University, Taoyuan City, Taiwan ) ; Wang, Hsin-Yao (Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan ) ; Chen, Chih-Kuang (School of Medicine, Chang Gung University, Taoyuan City, Taiwan ) ;
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    Abstract Objectives Prediction of activities of daily living (ADL) is crucial for optimized care of post-stroke patients. However, no suitably-validated and practical models are currently available in clinical practice. Methods Participants of a Post-acute Care-Cerebrovascular Diseases (PAC-CVD) program from a reference hospital in Taiwan between 2014 and 2016 were enrolled in this study. Based on 15 rehabilitation assessments, machine learning (ML) methods, namely logistic regression (LR), support vector machine (SVM), and random forest (RF), were used to predict the Barthel index (BI) status at discharge. Furthermore, SVM and linear regression were used to predict the actual BI scores at discharge. Results A total of 313 individuals (men: 208; women: 105) were enrolled in the study. All the classification models outperformed single assessments in predicting the BI statuses of the patients at discharge. The performance of the LR and RF algorithms was higher (area under ROC curve (AUC): 0.79) than that of SVM algorithm (AUC: 0.77). In addition, the mean absolute errors of both SVM and linear regression models in predicting the actual BI score at discharge were 9.86 and 9.95, respectively. Conclusions The proposed ML-based method provides a promising and practical computer-assisted decision making tool for predicting ADL in clinical practice. Highlights Prediction of activities of daily living (ADL) is crucial for post-stroke patients. No robust prediction models are currently available. A machine learning-based approach to predict ADL is proposed based on the assessments from rehabilitation ward of a reference hospital. ADL of post-stroke patients could be accurately predicted by the approach.


  • 주제어

    Stroke .   Activities of daily living .   Rehabilitation .   Machine learning .   Computer-assisted decision making.  

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