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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Access provided by NHS Data Safe Haven Dundee following ethical approval and completion of GDPR training.
References
Dhatariya, K., et al.: Nhs diabetes guideline for the perioperative management of the adult patient with diabetes. Diabet. Med. 29(4), 420–433 (2012)
Dros, J., Wewerinke, A., Bindels, P.J., van Weert, H.C.: Accuracy of monofilament testing to diagnose peripheral neuropathy: a systematic review. Ann. Family Med. 7(6), 555–558 (2009)
Guest, J.F., Fuller, G.W., Vowden, P.: Diabetic foot ulcer management in clinical practice in the uk: costs and outcomes. International Wound Journal 15(1), 43–52 (2018). https://doi.org/10.1111/iwj.12816, https://onlinelibrary.wiley.com/doi/abs/10.1111/iwj.12816
Kasbekar, P.U., Goel, P., Jadhav, S.P.: A decision tree analysis of diabetic foot amputation risk in indian patients. Front. Endocrinol. 8, 25 (2017)
Li, L., et al.: Serum albumin is associated with peripheral nerve function in patients with type 2 diabetes. Endocrine 50(2), 397–404 (2015)
National Institute for Health and Care Excellence: Diabetic foot problems: prevention and management (NICE Guideline NG19) (2015)
Sainsbury, C., Muir, R., Osmanska, J., Jones, G.: Machine learning (neural network)-driven algorithmic classification of type 1 or type 2 diabetes at the time of presentation significantly outperforms experienced clinician classification. In: Diabetic Medicine. vol. 35, pp. 168–168. Wiley (2018)
Schäfer, Z., Mathisen, A., Svendsen, K., Engberg, S., Rolighed Thomsen, T., Kirketerp-Møller, K.: Towards machine learning based decision support in diabetes care: A risk stratification study on diabetic foot ulcer and amputation. Front. Med. 7, 957 (2020)
Sharma, V.K., Joshi, M.V., Vishnoi, A.A., et al.: Interrelation of retinopathy with peripheral neuropathy in diabetes mellitus. J. Clin. Ophthalmol. Res. 4(2), 83 (2016)
Snyder, R.J., Hanft, J.R.: Diabetic foot ulcers-effects on qol, costs, and mortality and the role of standard wound care and advanced-care therapies. Ostomy Wound Manage. 55(11), 28–38 (2009)
Tulloch, J., Zamani, R., Akrami, M.: Machine learning in the prevention, diagnosis and management of diabetic foot ulcers: a systematic review. IEEE Access 8, 198977–199000 (2020)
Yazdanpanah, L., Nasiri, M., Adarvishi, S.: Literature review on the management of diabetic foot ulcer. World J. Diabetes 6(1), 37 (2015)
Acknowledgements
This work was funded by SBRI Challenge: Delivering Safer and Better Care Every Time for Patients with Diabetes. The authors would like to thank NHS Data Safe Haven Dundee for providing access to SCI-Diabetes, and Walk With Path Ltd as a partner on this project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Martin, K., Upadhyay, A., Wijekoon, A., Wiratunga, N., Massie, S. (2023). Machine Learning for Risk Stratification of Diabetic Foot Ulcers Using Biomarkers. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14075. Springer, Cham. https://doi.org/10.1007/978-3-031-36024-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-36024-4_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36023-7
Online ISBN: 978-3-031-36024-4
eBook Packages: Computer ScienceComputer Science (R0)