Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation. (March 2018)

• Record Type: Journal Article
• Authors: Lin, Wan-Yin; Chen, Chun-Hsien; Tseng, Yi-Ju; Tsai, Yu-Ting; Chang, Ching-Yu; Wang, Hsin-Yao; Chen, Chih-Kuang
• Is Part Of: International journal of medical informatics. Volume 111(2018)
• Page Start: 159
• Page End: 164
• Publication Date: 2018-03
• Subjects: Stroke -- Activities of daily living -- Rehabilitation -- Machine learning -- Computer-assisted decision making; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2018.01.002
• Languages: English
• ISSNs: 1386-5056

Abstract

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. 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.