Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach.

Journal: Journal of neuroengineering and rehabilitation
Published Date:

Abstract

BACKGROUND: In clinical practice, therapists often rely on clinical outcome measures to quantify a patient's impairment and function. Predicting a patient's discharge outcome using baseline clinical information may help clinicians design more targeted treatment strategies and better anticipate the patient's assistive needs and discharge care plan. The objective of this study was to develop predictive models for four standardized clinical outcome measures (Functional Independence Measure, Ten-Meter Walk Test, Six-Minute Walk Test, Berg Balance Scale) during inpatient rehabilitation.

Authors

  • Yaar Harari
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, 355 E. Erie St., Chicago, IL, 60611, USA.
  • Megan K O'Brien
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Rehabilitation Institute of Chicago, Chicago, IL, United States.
  • Richard L Lieber
    Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611, USA.
  • Arun Jayaraman
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611 USA.