Abstract
In the context of the ongoing COVID-19 pandemic, the need for robust health monitoring systems has become increasingly evident, especially for at-risk patients. The latter refers to individuals who are more susceptible to severe illness or complications if infected with COVID-19 due to underlying health conditions, age, or other factors. To address this need, the proposed research aims to develop an intelligent health monitoring system called AI-LMS (AI-based Long-term Monitoring System) that focuses on patients in pandemics. The system will utilize IoMT (Internet of Medical Things) sensors, Machine Learning algorithms, and Mobile Cloud Computing to enable real-time identification and monitoring of at-risk patients. The suggested approach can be simply adaptable for use in various pandemic circumstances. Using COVID-19 as a case study, AI-LMS underscores the significance of robust health monitoring systems in pandemic conditions. It is separated into two phases: the first collects and processes health data using a multi-layer classifier to identify at-risk patients, whereas the second one is centered on monitoring at-risk patients with the help of IoMT sensors that provide data to a machine learning model. The model alerts healthcare professionals to any concerning trends. By making slight modifications, this research aims to design efficient health monitoring systems for pandemic situations, ultimately leading to improved patient outcomes and alleviating the burden on healthcare systems.
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Zendaoui, N., Bouchemal, N., Benabdelhafid, M. (2024). AI-LMS: AI-Based Long-Term Monitoring System for Patients in Pandemics: COVID-19 Case Study. In: Mosbah, M., Kechadi, T., Bellatreche, L., Gargouri, F. (eds) Model and Data Engineering. MEDI 2023. Lecture Notes in Computer Science, vol 14396. Springer, Cham. https://doi.org/10.1007/978-3-031-49333-1_20
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