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Alsareii, Saeed Ali, Raza, Mohsin, Alamri, Abdulrahman Manaa, Alasmari, Mansour Yousef, Irfan, Muhammad, Khan, Umar and Awais, Muhammad 
ORCID: https://orcid.org/0000-0001-6421-9245
(2022)
Machine learning and internet of things enabled monitoring of post-surgery patients: A pilot study.
Sensors, 22 (4).
ISSN 1424-8220
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Abstract
Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: The Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education, Kingdom of Saudi Arabia for this research through a grant (NU/IFC/ENT/01/020) under the institutional Funding Committee at Najran University, Kingdom of Saudi Arabia. |
| Uncontrolled Keywords: | artificial intelligence (ai),gradient boosting regression,healthcare,human activity classification (hac),internet of things (iot),machine learning (ml),obesity,patient monitoring,post-surgery recovery,ultra-reliable low latency communication (urllc),analytical chemistry,information systems,atomic and molecular physics, and optics,biochemistry,instrumentation,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1600/1602 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 17 Oct 2023 00:44 |
| Last Modified: | 10 Dec 2024 01:42 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/93300 |
| DOI: | 10.3390/s22041420 |
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