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Agreement and Reliability Analysis of Machine Learning Scaling and Wireless Monitoring in the Assessment of Acute Proximal Weakness by Experts and Non-Experts: A Feasibility Study

Authors
 Eunjeong Park  ;  Kijeong Lee  ;  Taehwa Han  ;  Hyo Suk Nam 
Citation
 JOURNAL OF PERSONALIZED MEDICINE, Vol.12(1) : 20, 2022-01 
Journal Title
JOURNAL OF PERSONALIZED MEDICINE
Issue Date
2022-01
Keywords
agreement analysis ; artificial intelligence ; decision-support system ; inter-rater reliability ; machine learning ; sensors ; stroke
Abstract
Assessing the symptoms of proximal weakness caused by neurological deficits requires the knowledge and experience of neurologists. Recent advances in machine learning and the Internet of Things have resulted in the development of automated systems that emulate physicians' assessments. The application of those systems requires not only accuracy in the classification but also reliability regardless of users' proficiency in the real environment for the clinical point-of-care and the personalized health management. This study provides an agreement and reliability analysis of using a machine learning-based scaling of Medical Research Council (MRC) proximal scores to evaluate proximal weakness by experts and non-experts. The system trains an ensemble learning model using the signals from sensors attached to the limbs of patients in a neurological intensive care unit. For the agreement analysis, we investigated the percent agreement of MRC proximal scores and Bland-Altman plots of kinematic features between the expert- and non-expert scaling. We also analyzed the intra-class correlation coefficients (ICCs) of kinematic features and Krippendorff's alpha of the observers' scaling for the reliability analysis. The mean percent agreement between the expert- and the non-expert scaling was 0.542 for manual scaling and 0.708 for autonomous scaling. The ICCs of kinematic features measured using sensors ranged from 0.742 to 0.850, whereas the Krippendorff's alpha of manual scaling for the three observers was 0.275. The autonomous assessment system can be utilized by the caregivers, paramedics, or other observers during an emergency to evaluate acute stroke patients.
Files in This Item:
T202200285.pdf Download
DOI
10.3390/jpm12010020
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
Yonsei Authors
Nam, Hyo Suk(남효석) ORCID logo https://orcid.org/0000-0002-4415-3995
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187930
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