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Supporting patient nutrition in critical care units

Soomro, Kamran; Pimenidis, Elias; McWilliams, Chris


Chris McWilliams


Critical Care Unit (CCU) patients often benefit from being referred to dietitians for various reasons. This can help improve recovery time, resulting in more effective utilisation of valuable resources within the NHS (National Health Service) in the United Kingdom. However, said resources are often in high demand with scarce availability. Therefore, in this paper we propose an AI-based dashboard that can help clinicians automatically identify such patients, thereby reducing workload as well as cognitive load on clinical staff. We have trained various machine learning classifiers using various physiological measures of CCU patients and have identified a Support Vector Machine (SVC) classifier as the best performing model (AUC: 0.78). Our investigation shows promise results that significantly improve quality of patient care within the NHS. In future we intend to undertake more extensive evaluation of the dashboard developed as well as extend this work to paediatric patients.


Soomro, K., Pimenidis, E., & McWilliams, C. (2022). Supporting patient nutrition in critical care units. In Engineering Applications of Neural Networks: EANN 2022: Engineering Applications of Neural Networks (128-136).

Conference Name 23rd International Conference on Engineering Applications of Neural Networks EANN2022
Conference Location Crete, Greece
Start Date Jun 17, 2022
End Date Jun 20, 2022
Acceptance Date Apr 12, 2022
Online Publication Date Jun 10, 2022
Publication Date 2022-06
Deposit Date Apr 27, 2022
Publicly Available Date Jun 11, 2023
Publisher Springer Verlag (Germany)
Volume 1600 CCIS
Pages 128-136
Series Title Communications in Computer and Information Science CCIS
Series ISSN 1865-0937; 1865-0929
Book Title Engineering Applications of Neural Networks: EANN 2022: Engineering Applications of Neural Networks
Chapter Number 11
ISBN 978-3-031-08223-8; 978-3-031-08222-1
Keywords Critical Care Units; Patient Referrals; Clinical Decision Support; Supervised Classification; KNN
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