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Identifying Similar Patients Using Self-Organising Maps: A Case Study on Type-1 Diabetes Self-care Survey Responses

Tirunagari, S, Poh, N, Hu, G and Windridge, D (2015) Identifying Similar Patients Using Self-Organising Maps: A Case Study on Type-1 Diabetes Self-care Survey Responses arXiv.

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Abstract

Diabetes is considered a lifestyle disease and a well managed self-care plays an important role in the treatment. Clinicians often conduct surveys to understand the self-care behaviors in their patients. In this context, we propose to use Self-Organising Maps (SOM) to explore the survey data for assessing the self-care behaviors in Type-1 diabetic patients. Specifically, SOM is used to visualize high dimensional similar patient profiles, which is rarely discussed. Experiments demonstrate that our findings through SOM analysis corresponds well to the expectations of the clinicians. In addition, our findings inspire the experts to improve their understanding of the self-care behaviors for their patients. The principle findings in our study show: 1) patients who take correct dose of insulin, inject insulin at the right time, 2) patients who take correct food portions undertake regular physical activity and 3) patients who eat on time take correct food portions.

Item Type: Article
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Tirunagari, SUNSPECIFIEDUNSPECIFIED
Poh, NUNSPECIFIEDUNSPECIFIED
Hu, GUNSPECIFIEDUNSPECIFIED
Windridge, DUNSPECIFIEDUNSPECIFIED
Date : 21 March 2015
Copyright Disclaimer : Copyright The Author(s) 2015. This is an arXiv version of the paper.
Uncontrolled Keywords : cs.CE, cs.CE, cs.AI
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 04 Nov 2016 14:31
Last Modified : 04 Nov 2016 14:31
URI: http://epubs.surrey.ac.uk/id/eprint/812306

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