An Early Infectious Disease Outbreak Detection Mechanism Based on Self-Recorded Data from People with Diabetes
People with diabetes experience elevated blood glucose (BG) levels at the time of an infection. We propose to utilize patientgathered information in an Electronic Disease Surveillance Monitoring Network (EDMON), which may support the identification of a cluster of infected people with elevated BG levels on a spatiotemporal basis. The system incorporates data gathered from diabetes apps, continuous glucose monitoring (CGM) devices, and other appropriate physiological indicators from people with type 1 diabetes. This paper presents a novel approach towards modeling of the individuals BG dynamics, a mechanism to track and detect deviations of elevated BG readings. The models were developed and validated using self-recorded data in the non-infection status using Dexcom CGM devices, from two type 1 diabetes individuals over a 1-month period. The models were also tested using simulated datasets, which resemble the individuals BG evolution during infections. The models accurately simulated the individuals normal BG fluctuations and further detected statistically significant BG elevations.
Blood Glucose Disease Outbreaks Diabetes Mellitus,Type 1
Ashenafi Zebene Woldaregay Eirik (A)rsand Taxiarchis Botsis Gunnar Hartvigsen
Department of Computer Science,University of Troms(o)-The Arctic University of Norway,Troms(o),Norwa Norwegian Centre for E-health Research,University Hospital of North Norway,Troms(o),Norway Department of Computer Science,University of Troms(o)-The Arctic University of Norway,Troms(o),Norwa
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
619-623
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）