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. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 9-12 Dec 2014, Orlando, FL., USA. ISBN 9781479945191. [Conference or Workshop Item] (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.

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