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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
- Source :
- Sensors, Vol 21, Iss 776, p 776 (2021), Sensors (Basel, Switzerland), Sensors, Volume 21, Issue 3
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
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&rsquo<br />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&ndash<br />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.
- Subjects :
- Technology
Computer science
Remote patient monitoring
Decision Tree
Vital signs
Decision tree
Poison control
02 engineering and technology
Machine learning
computer.software_genre
lcsh:Chemical technology
Biochemistry
Suicide prevention
Article
Analytical Chemistry
remote patient monitoring (RPM)
Respiratory Rate
0202 electrical engineering, electronic engineering, information engineering
Humans
Radio-frequency identification
lcsh:TP1-1185
Electrical and Electronic Engineering
Set (psychology)
Instrumentation
suicide
Monitoring, Physiologic
Ensemble Learning
Random Forest
business.industry
020206 networking & telecommunications
Linear Regression
equipment and supplies
Ensemble learning
Mental health
Atomic and Molecular Physics, and Optics
Random forest
Radio Frequency Identification Device
machine learning
020201 artificial intelligence & image processing
Artificial intelligence
business
radio frequency identification (RFID)
computer
mental health
XGBoost
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 776
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....3063551e4db44ee045721b408f6ea9e7