DUGi

Prediction of hyperglycaemia and hypoglycaemia events using longitudinal data [Pòster]

http://dugi.udg.edu/item/http:@@@@hdl.handle.net@@10256@@17875

Pòster de congrés presentat a: 11th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD 2018): 14-17 February, 2018, Vienna, Austria

In type 1 diabetes management, mobile health applications are becoming a cornerstone to empower people to self-manage their disease. There are many applications addressed to calculate insulin doses based on the current information (e.g. carbohydrates intake) and a few of them are accompanied by modules able to supervise postprandial conditions and recommend corrective actions if the user falls in an abnormal state (i.e. hyperglycaemia or hypoglycaemia). On the other hand, mobile apps favour the gathering of historical data from which machine learning techniquescanbeusedtopredictifuserconditionswillworsen. Thisworkpresentstheapplicationofk-nearestneighbouronthehistoricaldatagatheredonpatients,sothatgiven the information related to a sequence of meals, the method is able to predict if the patient will fall in an abnormal condition. The experimentation has been carried out with the UVA-Padova type 1 diabetes simulator over eleven adult profiles. Results corroborate that the use of sequential data improve significantly the prediction outcome when forecastsdistinguishthetypeofmeal(breakfast,lunchanddinner)

This work has received funding from the EU Horizon2020 research and innovation programme under grant agreement No689810 (PEPPER), and from the University of Girona under the grant MPCUdG2016 (Ajut per a la millora de la productivitat científica dels grups de recerca), and the Spanish MINECO under the grant number DPI2013-47450-C21-R. This work has been developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016)

Advanced Technologies & Treatments for Diabetes (ATTD)