عنوان انگلیسی مقاله:
Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests
ترجمه فارسی عنوان مقاله:
گروه های یادگیری ماشینی بیماران براساس احتمال بهبود عملکرد زودهنگام بر اساس سنسورهای پوشیدنی ابزار تست شده به موقع قبل و بعد از عمل
Sciencedirect - Elsevier - The Journal of Arthroplasty, 34 (2019) 2267-2271: doi:10:1016/j:arth:2019:05:061
Riley A. Bloomfield, BESc a, b, *, Harley A. Williams, MSc b, c, Jordan S. Broberg, BMSc b, c, Brent A. Lanting, MD, MSc, FRCSC d, Kenneth A. McIsaac, PhD a, Matthew G. Teeter, PhD b,
Background: Wearable sensors permit efficient data collection and unobtrusive systems can be used for
instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the
abundant information these systems provide and segment patients into relevant groups without specifying
group membership criteria. The objective of this study is to examine functional parameters
influencing favorable recovery outcomes by separating patients into functional groups and tracking them
through clinical follow-ups.
Methods: Patients undergoing primary unilateral total knee arthroplasty (n ¼ 68) completed instrumented
timed-up-and-go tests preoperatively and at their 2-, 6-, and 12-week follow-up appointments.
A custom wearable system extracted 55 metrics for analysis and a K-means algorithm separated patients
into functionally distinguished groups based on the derived features. These groups were analyzed to
determine which metrics differentiated most and how each cluster improved during early recovery.
Results: Patients separated into 2 clusters (n ¼ 46 and n ¼ 22) with significantly different test completion
times (12.6 s vs 21.6 s, P < .001). Tracking the recovery of both groups to their 12-week follow-ups
revealed 64% of one group improved their function while 63% of the other maintained preoperative
function. The higher improvement group shortened their test times by 4.94 s, (P ¼ .005) showing faster
recovery while the other group did not improve above a minimally important clinical difference (0.87 s,
P ¼.07). Features with the largest effect size between groups were distinguished as important functional
Conclusion: This work supports using wearable sensors to instrument functional tests during clinical
visits and using machine learning to parse complex patterns to reveal clinically relevant parameters.
Keywords: total knee arthroplasty | wearable sensors | machine learning | functional testing | early recovery