MACHINE LEARNING-BASED PREDICTION OF FUNCTIONAL RECOVERY IN STROKE PATIENTS USING PHYSIOTHERAPY AND CLINICAL ASSESSMENT DATA

Authors

  • S Jagadeesh, Kumbala Pradeep Reddy, B. Narendra Kumar Author

DOI:

https://doi.org/10.48047/2a3xnx81

Keywords:

Stroke Rehabilitation, Functional Recovery Prediction, Machine Learning, Physiotherapy Analytics, Clinical Assessment, Explainable Artificial Intelligence.

Abstract

Stroke rehabilitation aims to restore functional independence and improve quality of life through structured physiotherapy interventions. Accurate prediction of functional recovery outcomes is important for  rehabilitation planning, resource allocation, and personalized treatment design.

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References

B. H. Dobkin, Rehabilitation After Stroke, 2nd ed. New York, NY, USA: Oxford University Press, 2014.

T. G. Hornby, J. M. Reisman, J. H. Ward, et al., “Clinical practice guideline to improve locomotor function following chronic stroke, incomplete spinal cord injury, and brain injury,” Journal of Neurologic Physical Therapy, vol. 44, no. 1, pp. 49–100, 2020.

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Published

2025-03-20

Issue

Vol. 54 No. 3 (2025): Year 2025, Volume 54, Issue 3

Section

Original

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How to Cite

MACHINE LEARNING-BASED PREDICTION OF FUNCTIONAL RECOVERY IN STROKE PATIENTS USING PHYSIOTHERAPY AND CLINICAL ASSESSMENT DATA (S Jagadeesh, Kumbala Pradeep Reddy, B. Narendra Kumar , Trans.). (2025). Cuestiones De Fisioterapia, 54(3), 5450-5458. https://doi.org/10.48047/2a3xnx81