Abstract
Patient sensing and data analytics provide information that plays an important role in the patient care process. Patterns identified from data and Machine Learning (ML) algorithms can identify risk/abnormal patients’ data. Due to automatization this process can reduce workload of medical staff, as the algorithms alert for possible problems. We developed an integrated approach to monitor patients’ temperature applied to COVID-19 elderly patients and an ML process to identify abnormal behavior with alerts to physicians.
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Acknowledgment
Project ‘ReATeC - Remote Assessment and Telemonitoring of COVID-19 (2020)’. Financed by ‘AAC 15/SI/2020. Sistema de Incentivos de Actividades de Investigação e Desenvolvimento e Investimento em Infraestruturas de Ensaio e Optimização (upscaling) no Contexto de Covid-19. Portugal 2020. I&D Empresas - COVID-19’.
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Elvas, L.B. et al. (2023). Wearable Temperature Sensor and Artificial Intelligence to Reduce Hospital Workload. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_73
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