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Machine learning for risk stratification of diabetic foot ulcers using biomarkers.

Martin, Kyle; Upadhyay, Ashish; Wijekoon, Anjana; Wiratunga, Nirmalie; Massie, Stewart

Authors



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

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