Abstract
Critical Care Units (CCU) in a hospital treat the severely sick patients that need constant monitoring and close medical attention. Feeding patients, enteral feeding in particular, is a critical and continuous process. Monitoring patients, managing their feeding and referring to a dietician is a key factor in CCUs. Screening patients for referral to a dietician in a CCU is an error-prone and complicated task. One of the main challenges in this regard is that the data needed to screen patients is scattered among many different variables and textual forms. The number of patients being treated in the CCU is also a significant problem since it becomes difficult for the staff to keep track of the needs of all patients. Therefore, an automated screening tool can support effectively the feeding process and contribute considerably towards improving the quality and consistency of patient care. In this paper we present early stages of a project that aims at using machine learning techniques to help CCU consultants to automatically screen patients for dietician referral.
Supported by the University of the West of England, Bristol.
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Acknowledgements
We would like to acknowledge the support and help provided by Dr Chris McWilliams, Clinicians, Consultants, and other clinical researchers from the Critical Care Unit at Bristol Royal Infirmary.
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Soomro, K., Pimenidis, E. (2020). Automated Screening of Patients for Dietician Referral. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_24
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