Dr Kamran Soomro Kamran.Soomro@uwe.ac.uk
Associate Professor of Artificial Intelligence
Automated screening of patients for dietician referral
Soomro, Kamran; Pimenidis, Elias
Authors
Dr Elias Pimenidis Elias.Pimenidis@uwe.ac.uk
Senior Lecturer in Computer Science
Contributors
L. Iliadis
Editor
P. Angelov
Editor
C. Jayne
Editor
Dr Elias Pimenidis Elias.Pimenidis@uwe.ac.uk
Editor
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.
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | EANN 2020 21st International Conference on Engineering Applications of Neural Networks |
Start Date | Jun 5, 2020 |
End Date | Jun 7, 2020 |
Acceptance Date | May 1, 2020 |
Online Publication Date | May 28, 2020 |
Publication Date | May 28, 2020 |
Deposit Date | Sep 18, 2020 |
Publicly Available Date | May 29, 2021 |
Series Title | Proceedings of the International Neural Networks Society |
Book Title | Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference |
ISBN | 9783030487904 |
DOI | https://doi.org/10.1007/978-3-030-48791-1_24 |
Public URL | https://uwe-repository.worktribe.com/output/5973778 |
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Copyright Statement
This is a post-peer-review, pre-copyedit version of an article published in Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-48791-1_24
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