Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses

Tirunagari, Santosh, Poh, Norman, Aliabadi, Kouros, Windridge, David ORCID: https://orcid.org/0000-0001-5507-8516 and Cooke, Deborah (2014) Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses. In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 9-12 Dec 2014, Orlando, FL., USA. (doi:10.1109/CIDM.2014.7008682)

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Abstract

Survey questionnaires are often heterogeneous because they contain both quantitative (numeric) and qualitative (text) responses, as well as missing values. While traditional, model-based methods are commonly used by clinicians, we deploy Self Organizing Maps (SOM) as a means to visualise the data. In a survey study aiming at understanding the self-care behaviour of 611 patients with Type-1 Diabetes, we show that SOM can be used to (1) identify co-morbidities; (2) to link self-care factors that are dependent on each other; and (3) to visualise individual patient profiles; In evaluation with clinicians and experts in Type-1 Diabetes, the knowledge and insights extracted using SOM correspond well to clinical expectation. Furthermore, the output of SOM in the form of a U-matrix is found to offer an interesting alternative means of visualising patient profiles instead of a usual tabular form.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 19498
Notes on copyright: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: David Windridge
Date Deposited: 22 Apr 2016 10:58
Last Modified: 03 Jun 2019 12:07
ISBN: 9781479945191
URI: https://eprints.mdx.ac.uk/id/eprint/19498

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