article · Electronics
Managing infectious disease outbreaks such as COVID-19 requires integrated tools for tracking patients and identifying infections rapidly. Existing commercial systems often monitor patients in isolation rather than offering an end-to-end disease management pathway. A multi-layered architecture addresses this challenge by combining wearable sensors, mobile software, fog and cloud computing, and clinical decision support. The framework operates across three distinct tiers: a patient layer, a cloud layer, and a hospital layer. In the patient layer, wearable sensors and a mobile application monitor individuals in real time. The cloud layer uses fog networking to handle data storage and transmission efficiently. Finally, the hospital layer analyses chest X-ray images using a convolutional neural network with transfer learning. When tested, the diagnostic model attained an accuracy of 97.95 per cent and a specificity of 98.85 per cent, supporting clinical decisions and helping curb healthcare costs.
End-to-end monitoring and diagnosis tools help clinicians make timely decisions and manage infectious disease patients more safely. By connecting home-based wearable sensors with hospital-level artificial intelligence analysis, healthcare providers can track patient conditions continuously, detect infections from medical scans with high accuracy, and potentially lower overall care costs during widespread epidemic outbreaks.
This technology targets healthcare providers and clinical monitoring services seeking comprehensive systems for remote patient tracking and rapid disease detection. It combines wearable sensor applications with diagnostic software for hospital radiology workflows. The framework represents an applied and tested model with high experimental diagnostic accuracy, but real-world deployment would require full integration across medical devices, telecommunications infrastructure, and hospital clinical data systems.
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Coronavirus (COVID-19) is a new virus of viral pneumonia. It can outbreak in the world through person-to-person transmission. Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease. The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems. The wireless body area network, cloud computing, fog computing, and clinical decision support system are integrated to provide a comprehensive and complete model for disease detection and monitoring. By monitoring a person with COVID-19 in real time, physicians can guide patients with the right decisions. The proposed framework has three main layers (i.e., a patient layer, cloud layer, and hospital layer). In the patient layer, the patient is tracked through a set of wearable sensors and a mobile app. In the cloud layer, a fog network architecture is proposed to solve the issues of storage and data transmission. In the hospital layer, we propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient’s X-ray scan images and transfer learning. The proposed model achieved promising results compared to the state-of-the art (i.e., accuracy of 97.95% and specificity of 98.85%). Our framework is a useful application, through which we expect significant effects on COVID-19 proliferation and considerable lowering in healthcare expenses.
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DOI: 10.3390/electronics9091439
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