Konferensartikel

Infection incidences in people with diabetes can create sever health complications mainly due to the effect of stress hormones, such as cortisol and adrenaline, which increases glucose production and insulin resistance in the body. The proposed electronic disease surveillance monitoring network (EDMON) relies on self-recorded data from people with Type 1 diabetes and dedicated algorithms to detect infection incidence at individual level and uncover infection outbreaks at population level. EDMON incorporates four major modules; patient modules, mobile computing modules, computing modules (cloud backend), and end user modules. This paper presents the patient and computing module prototypes along with various essential design choices and challenges together with their solution. At the time of writing, development of the EDMON infection and outbreak detection algorithms are already completed and the next phase of the study involves integration of the prototype along with the EDMON algorithms, developing end user visualization mechanism and performing a pilot study.

Type 1 diabetes, Infection detection, Outbreak detection, Patient module, Cloud backend solution