Using Machine Learning to Plan Rehabilitation for Home Care Clients: Beyond “Black-Box” Predictions

  • Mu ZhuEmail author
  • Lu Cheng
  • Joshua J. Armstrong
  • Jeff W. Poss
  • John P. Hirdes
  • Paul Stolee
Part of the Intelligent Systems Reference Library book series (ISRL, volume 56)


Resistance to adopting machine-learning algorithms in clinical practice may be due to a perception that these are “black-box” techniques and incompatible with decision-making based on evidence and clinical experience. We believe this resistance is unfortunate, given the increasing availability of large databases containing assessment information that could benefit from machine-learning and data-mining techniques, thereby providing a new and important source of evidence upon which to base clinical decisions. We have focused our investigation on the clinical applications of machine-learning algorithms on older persons in a home care rehabilitation setting. Data for this research were obtained from standardized client assessments using the comprehensive RAI-Home Care (RAI-HC) assessment instrument. Our work has shown that machine-learning algorithms can produce better decisions than standard clinical protocols. More importantly, we have shown that machine-learning algorithms can do much more than make “black-box” predictions; they can generate important new clinical and scientific insights. These insights can be used to make better decisions about treatment plans for patients and about resource allocation for healthcare services, resulting in better outcomes for patients, and in a more efficient and effective healthcare system.


Support Vector Machine Home Care Frailty Index Ensemble Approach Random Forest Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The InfoRehab project is supported by the Canadian Institutes of Health Research (CIHR). We thank Chloe Wu for her assistance with the management of data.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Mu Zhu
    • 1
    Email author
  • Lu Cheng
    • 1
  • Joshua J. Armstrong
    • 2
  • Jeff W. Poss
    • 2
  • John P. Hirdes
    • 2
  • Paul Stolee
    • 2
  1. 1.Department of Statistics and Actuarial ScienceUniversity of WaterlooWaterlooCanada
  2. 2.School of Public Health and Health SystemsUniversity of WaterlooWaterlooCanada

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