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

Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Shaik, Thanveer Basha and Higgins, Niall and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Zhou, Xujuan (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 (3):776. pp. 1-20. ISSN 1424-8220

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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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 29 Jan 2021 05:34
Last Modified: 03 Feb 2021 03:15
Uncontrolled Keywords: remote patient monitoring (RPM); radio frequency identification (RFID); machine learning; linear regression; decision tree; Random Forest; XGBoost; Ensemble Learning; mental health; suicide
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
11 Medical and Health Sciences > 1117 Public Health and Health Services > 111714 Mental Health
Fields of Research (2020): 42 HEALTH SCIENCES > 4203 Health services and systems > 420308 Health informatics and information systems
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460301 Active sensing
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460308 Pattern recognition
Socio-Economic Objectives (2008): C Society > 92 Health > 9202 Health and Support Services > 920209 Mental Health Services
E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Identification Number or DOI: https://doi.org/10.3390/s21030776
URI: http://eprints.usq.edu.au/id/eprint/40973

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