A Predictive Mobility Limitation Algorithm
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The goal of this project is to advance research on mobility limited populations by developing and validating a method to reliably classify mobility limitation-severity within large healthcare datasets. Using innovative machine learning techniques and healthcare administrative data on veterans enrolled in VA healthcare, we will develop and validate an algorithm to accurately identify and distinguish between individuals with differing levels of mobility limitation (none to severe).
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UIC College of Applied Health Science Interdisciplinary Pilot Grant
Previous funding provided by the Center for Large Data Research and Data Sharing in Rehabilitation UTMB sub-award No. 18-015
IRB information
IRB is part of a larger study called the Weight and Veterans Environments Study.
Acknowledgment of VA support:
This material is the result of work supported with resources and the use of facilities at the Edward Hines, Jr. VA Hospital.
Disclaimer:
The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.
Publications and resources Heading link
Eisenberg, Y., Powell, L. M., Zenk, S. N., & Tarlov, E. (2020). Development of a Predictive Algorithm to Identify Adults With Mobility Limitations Using VA Health Care Administrative Data. Medical Care Research and Review, 1077558720950880.
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Principal investigator
Yochai Eisenberg, UIC College of Applied Health Sciences
Co-investigators
Jiehuan Sun, UIC School of Public Health