An adaptive medical cyber-physical system for post diagnosis patient care using cloud computing and machine learning approach

• Main Authors: Miah, Md Saef Ullah, Sarwar, Talha, Islam, Saima Sharleen, Haque, Md Samiul, Masuduzzaman, Md, Bhowmik, Abhijit
• Format: Conference or Workshop Item
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/39429/1/An%20adaptive%20Medical%20Cyber-Physical%20System%20for%20post%20diagnosis.pdf; http://umpir.ump.edu.my/id/eprint/39429/2/An%20adaptive%20medical%20cyber-physical%20system%20for%20post%20diagnosis%20patient%20care%20using%20cloud%20computing%20and%20machine%20learning%20approach_ABS.pdf; http://umpir.ump.edu.my/id/eprint/39429/; https://doi.org/10.1109/INCET54531.2022.9824032

Medical care is one of the most basic human needs. Due to the global shortage of doctors, nurses, and other healthcare personnel, medical cyber-physical systems are quickly becoming a viable option. Post-diagnosis surveillance is an essential application of these systems, which can be performed more successfully using various monitoring devices rather than active observation by nurses in their physical presence. However, most existing solutions for this application are rigid and do not consider current difficulties. Intelligent and adaptive systems can overcome the challenges because of the advances in relevant technology, especially healthcare 4.0. Therefore, this work presents an adaptive system based on cloud and edge computing architecture and machine learning approaches to perform post-diagnosis medical tasks on patients, thus reducing the need for nurses, especially in the post-diagnosis phase.