ABSTRACT
With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, and the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices and cloud computing services, and basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, and generate multivariate data to provide just-in-time healthcare services. In this article, we present a novel collaborative disease detection system based on IoMT amalgamated with captured image data. The system can be based on intelligent agents, where every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared with baseline solutions for disease detection. © 2001-2012 IEEE.
ABSTRACT
With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, as well as the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices as well as cloud computing services, as well as basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, as well as generate multivariate data to provide just-in-time healthcare services. In this paper, we present a novel collaborative disease detection system based on IoMT as well as captured image data. The system can be based on intelligent agents, where each and every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared to baseline solutions for disease detection. IEEE