Identifying Similar Patients Using Self-Organising Maps: A Case Study on Type-1 Diabetes Self-care Survey Responses
This work helps clinicians better understand patient self-care behaviors in Type-1 diabetes, but it is incremental as it applies an existing method to a new dataset.
The study used Self-Organising Maps to analyze Type-1 diabetes self-care survey data, identifying patient groups with specific behaviors such as correct insulin dosing and timely eating, which aligned with clinician expectations.
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.