Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Tao, Xiaohui; Shaik, Thanveer Basha; Higgins, Niall; Gururajan, Raj; Zhou, Xujuan

doi:10.3390/s21030776

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Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

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School of Sciences, University of Southern Queensland, Toowoomba 4350, Australia

Metro North Hospital and Health Service, Royal Brisbane and Women’s Hospital, Herston 4029, Australia

School of Management and Enterprise, University of Southern Queensland, Springfield 4300, Australia

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School of Nursing, Queensland University of Technology, Brisbane 4000, Australia

Author to whom correspondence should be addressed.

Academic Editors: Zongyuan Ge, Yingying Zhu and Xiaofeng Zhu

Sensors 2021, 21(3), 776; https://doi.org/10.3390/s21030776

Received: 21 December 2020 / Revised: 20 January 2021 / Accepted: 20 January 2021 / Published: 24 January 2021

(This article belongs to the Special Issue Healthcare Monitoring and Management with Artificial Intelligence)

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Abstract

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward. View Full-Text

Keywords: remote patient monitoring (RPM); radio frequency identification (RFID); machine learning; linear regression; decision tree; Random Forest; XGBoost; Ensemble Learning; mental health; suicide

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Tao, X.; Shaik, T.B.; Higgins, N.; Gururajan, R.; Zhou, X. Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities. Sensors 2021, 21, 776. https://doi.org/10.3390/s21030776

AMA Style

Tao X, Shaik TB, Higgins N, Gururajan R, Zhou X. Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities. Sensors. 2021; 21(3):776. https://doi.org/10.3390/s21030776

Chicago/Turabian Style

Tao, Xiaohui, Thanveer B. Shaik, Niall Higgins, Raj Gururajan, and Xujuan Zhou. 2021. "Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities" Sensors 21, no. 3: 776. https://doi.org/10.3390/s21030776

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Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

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