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

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

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].

• Presentation Conference Type: Conference Paper (published)
• Conference Name: International conference on computational science 2023 (ICCS 2023): computing at the cutting edge of science
• 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
• Peer Reviewed: Peer Reviewed
• 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