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Computerized Adaptive Testing System of Functional Assessment of Stroke
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Published on: January 7, 2019

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Using Machine Learning to Develop a Short-Form Measure Assessing 5 Functions in Patients With Stroke.

Gong-Hong Lin1, Chih-Ying Li2, Ching-Fan Sheu3

  • 1Master Program in Long-Term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan.

Archives of Physical Medicine and Rehabilitation
|January 3, 2022
PubMed
Summary
This summary is machine-generated.

A new 15-item machine learning tool, the ML-5F, efficiently assesses key functions like mobility and balance in stroke survivors. It shows strong validity and responsiveness for clinical use.

Keywords:
Activities of daily livingMachine learningPostural balanceRehabilitationStroke

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Area of Science:

  • Neurology
  • Rehabilitation Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Stroke significantly impacts functional recovery, necessitating accurate and efficient assessment tools.
  • Current functional assessments can be lengthy, posing challenges in busy clinical settings.
  • The need for a concise yet comprehensive measure of motor function and mobility in stroke patients is critical.

Purpose of the Study:

  • To develop and validate a brief, machine learning-based measure (ML-5F) for assessing five key functions in stroke patients.
  • To evaluate the concurrent validity and responsiveness of the ML-5F against established stroke assessment scales.
  • To establish the ML-5F as a potentially efficient outcome measure for stroke rehabilitation.

Main Methods:

  • Utilized secondary data from 307 stroke patients.
  • Employed Extreme Gradient Boosting machine learning to select 15 items from the Barthel Index (BI), Postural Assessment Scale for Stroke (PASS), and Stroke Rehabilitation Assessment of Movement (STREAM).
  • Transformed raw scores of selected items into ML-5F scores and analyzed validity and responsiveness.

Main Results:

  • The ML-5F, comprising 15 items, demonstrated strong concurrent validity with Pearson's r values ranging from 0.88 to 0.98.
  • The measure exhibited good responsiveness, with standardized response means between 0.28 and 1.01.
  • The ML-5F effectively assesses activities of daily living (ADL), balance, upper extremity (UE), lower extremity (LE) motor function, and mobility.

Conclusions:

  • The ML-5F is a short, 15-item measure with sufficient concurrent validity and responsiveness for assessing multiple functional domains in stroke patients.
  • The ML-5F holds significant potential as an efficient and reliable outcome measure in clinical stroke rehabilitation settings.
  • This machine learning-derived tool offers a promising alternative for streamlined functional assessment post-stroke.