Dr Kyle Martin k.martin3@rgu.ac.uk
Lecturer
Machine learning for risk stratification of diabetic foot ulcers using biomarkers.
Martin, Kyle; Upadhyay, Ashish; Wijekoon, Anjana; Wiratunga, Nirmalie; Massie, Stewart
Authors
ASHISH UPADHYAY a.upadhyay@rgu.ac.uk
Research Student
Dr Anjana Wijekoon a.wijekoon1@rgu.ac.uk
Research Fellow B
Professor Nirmalie Wiratunga n.wiratunga@rgu.ac.uk
Associate Dean for Research
Dr Stewart Massie s.massie@rgu.ac.uk
Associate Professor
Abstract
Development of a Diabetic Foot Ulcer (DFU) causes a sharp decline in a patient's health and quality of life. The process of risk stratification is crucial for informing the care that a patient should receive to help manage their Diabetes before an ulcer can form. In existing practice, risk stratification is a manual process where a clinician allocates a risk category based on biomarker features captured during routine appointments. We present the preliminary outcomes of a feasibility study on machine learning techniques for risk stratification of DFU formation. Our findings highlight the importance of considering patient history, and allow us to identify biomarkers which are important for risk classification.
Citation
MARTIN, K., UPHADYAY, A., WIJEKOON, A., WIRATUNGA, N. and MASSIE, S. [2023]. Machine learning for risk stratification of diabetic foot ulcers using biomarkers. To be presented at the 2023 International conference on computational science (ICCS 2023): computing at the cutting edge of science, 3-5 July 2023, Prague, Czech Republic: [virtual event].
Conference Name | International conference on computational science 2023 (ICCS 2023): computing at the cutting edge of science |
---|---|
Conference Location | Prague, Czech Republic: [virtual event] |
Start Date | Jul 3, 2023 |
End Date | Jul 5, 2023 |
Acceptance Date | Apr 4, 2023 |
Online Publication Date | Jun 26, 2023 |
Publication Date | Dec 31, 2023 |
Deposit Date | Apr 25, 2023 |
Publicly Available Date | Jun 27, 2024 |
Publisher | Springer |
Pages | 153-161 |
Series Title | Lecture Notes in Computer Science (LNCS) |
Series ISSN | 0302-9743; 1611-3349 |
ISBN | 9783031360237 |
DOI | https://doi.org/10.1007/978-3-031-36024-4_11 |
Keywords | Diabetic foot ulceration; Machine learning; Biomarkers |
Public URL | https://rgu-repository.worktribe.com/output/1937818 |
Files
This file is under embargo until Jun 27, 2024 due to copyright reasons.
Contact publications@rgu.ac.uk to request a copy for personal use.
You might also like
Evaluating a pass/fail grading model in first year undergraduate computing.
(2023)
Conference Proceeding
CBR driven interactive explainable AI.
(2023)
Conference Proceeding
iSee: intelligent sharing of explanation experience of users for users.
(2023)
Conference Proceeding
iSee: demonstration video. [video recording]
(2023)
Digital Artefact
Clinical dialogue transcription error correction using Seq2Seq models.
(2022)
Conference Proceeding
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search