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

Journal: International journal of medical informatics
Published Date: Jan 4, 2018

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.

Authors

  • Wan-Yin Lin
    Department of Physical Medicine & Rehabilitation, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.
  • Chun-Hsien Chen
    Department of Information Management, Chang Gung University, Taoyuan City, Taiwan.
  • Yi-Ju Tseng
    Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.
  • Yu-Ting Tsai
    School of Medicine, Chang Gung University, Taoyuan City, Taiwan.
  • Ching-Yu Chang
    School of Medicine, Chang Gung University, Taoyuan City, Taiwan.
  • Hsin-Yao Wang
    Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.
  • Chih-kuang Chen
    Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Taoyuan, Taoyuan, Taiwan.

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