A Software Framework for Remote Patient Monitoring by Using Multi-Agent Systems Support

Abstract

BackgroundAlthough there have been significant advances in network, hardware, and software technologies, the health care environment has not taken advantage of these developments to solve many of its inherent problems. Research activities in these 3 areas make it possible to apply advanced technologies to address many of these issues such as real-time monitoring of a large number of patients, particularly where a timely response is critical.ObjectiveThe objective of this research was to design and develop innovative technological solutions to offer a more proactive and reliable medical care environment. The short-term and primary goal was to construct IoT4Health, a flexible software framework to generate a range of Internet of things (IoT) applications, containing components such as multi-agent systems that are designed to perform Remote Patient Monitoring (RPM) activities autonomously. An investigation into its full potential to conduct such patient monitoring activities in a more proactive way is an expected future step.MethodsA framework methodology was selected to evaluate whether the RPM domain had the potential to generate customized applications that could achieve the stated goal of being responsive and flexible within the RPM domain. As a proof of concept of the software framework’s flexibility, 3 applications were developed with different implementations for each framework hot spot to demonstrate potential. Agents4Health was selected to illustrate the instantiation process and IoT4Health’s operation. To develop more concrete indicators of the responsiveness of the simulated care environment, an experiment was conducted while Agents4Health was operating, to measure the number of delays incurred in monitoring the tasks performed by agents.ResultsIoT4Health’s construction can be highlighted as our contribution to the development of eHealth solutions. As a software framework, IoT4Health offers extensibility points for the generation of applications. Applications can extend the framework in the following ways: identification, collection, storage, recovery, visualization, monitoring, anomalies detection, resource notification, and dynamic reconfiguration. Based on other outcomes involving observation of the resulting applications, it was noted that its design contributed toward more proactive patient monitoring. Through these experimental systems, anomalies were detected in real time, with agents sending notifications instantly to the health providers.ConclusionsWe conclude that the cost-benefit of the construction of a more generic and complex system instead of a custom-made software system demonstrated the worth of the approach, making it possible to generate applications in this domain in a more timely fashion.

Agent-Oriented Methodologies Evaluation Frameworks: A Review

“…For example, [91] reviews agent-based cloud computing, [92] and [93] review the agent-based internet of things and cyber-physical systems and [94] reviews the applications of MAS in digital factories. By reviewing some of the applications proposed in the literature for MAS in these emerging technologies, one can con¯rm the vast potential of future work, such as smart vehicles and tra±c control systems [95][96][97][98], personalized medicine and healthcare systems [99][100][101], security and safety [102,103] and smart manufacturing [104,105]. This coupling can be attributed to the unique characteristics considered for agents and their capabilities for interaction.…”

A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study

“…Due to the popularity of the system, versatile extensions are available (eg, a powerful camera and hygienic housings). Indeed, Raspberry Pi has seen widespread use in different Internet of Things (IoT) applications in the healthcare sector [9-11].…”

Screening for Atrial Fibrillation and the Role of Digital Health Technologies

“…There has even been suggestion more recently that it can be used to predict when AF will occur up to an hour before the event in those with non-permanent AF [82,83]. AI has many more potential benefits, from helping gather the most useful data on a patient before a consultation to outpatient monitoring and subsequent prioritisation [36, [84][85][86][87].…”