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Article

Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device

1
Department of Electrical Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan
2
Division of Cardiology, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
*
Author to whom correspondence should be addressed.
Biosensors 2022, 12(8), 605; https://doi.org/10.3390/bios12080605
Received: 10 June 2022 / Revised: 28 July 2022 / Accepted: 3 August 2022 / Published: 5 August 2022
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
Chronic obstructive pulmonary disease (COPD) is a significantly concerning disease, and is ranked highest in terms of 30-day hospital readmission. Generally, physical activity (PA) of daily living reflects the health status and is proposed as a strong indicator of 30-day hospital readmission for patients with COPD. This study attempted to predict 30-day hospital readmission by analyzing continuous PA data using machine learning (ML) methods. Data were collected from 16 patients with COPD over 3877 days, and clinical information extracted from the patients’ hospital records. Activity-based parameters were conceptualized and evaluated, and ML models were trained and validated to retrospectively analyze the PA data, identify the nonlinear classification characteristics of different risk factors, and predict hospital readmissions. Overall, this study predicted 30-day hospital readmission and prediction performance is summarized as two distinct approaches: prediction-based performance and event-based performance. In a prediction-based performance analysis, readmissions predicted with 70.35% accuracy; and in an event-based performance analysis, the total 30-day readmissions were predicted with a precision of 72.73%. PA data reflect the health status; thus, PA data can be used to predict hospital readmissions. Predicting readmissions will improve patient care, reduce the burden of medical costs burden, and can assist in staging suitable interventions, such as promoting PA, alternate treatment plans, or changes in lifestyle to prevent readmissions. View Full-Text
Keywords: COPD; readmission prediction; physical activity; activity index; machine learning; hospital readmission; COVID-19 COPD; readmission prediction; physical activity; activity index; machine learning; hospital readmission; COVID-19
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MDPI and ACS Style

Verma, V.K.; Lin, W.-Y. Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device. Biosensors 2022, 12, 605. https://doi.org/10.3390/bios12080605

AMA Style

Verma VK, Lin W-Y. Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device. Biosensors. 2022; 12(8):605. https://doi.org/10.3390/bios12080605

Chicago/Turabian Style

Verma, Vijay Kumar, and Wen-Yen Lin. 2022. "Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device" Biosensors 12, no. 8: 605. https://doi.org/10.3390/bios12080605

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