title = "Using Grammatical Evolution to Generate Short-term
Blood Glucose Prediction Models",
booktitle = "KDH@IJCAI-ECAI 2018 The 3rd International Workshop on
Knowledge Discovery in Healthcare Data",
year = "2018",
editor = "Kerstin Bach and Razvan C. Bunescu and
Oladimeji Farri and Aili Guo and Sadid A. Hasan and Zina M. Ibrahim and
Cindy Marling and Jesse Raffa and Jonathan Rubin and
Honghan Wu",
abstract = "Blood glucose levels prediction provides the
possibility to issue early warnings related to
ineffective or poor treatments. Advance notifications
of adverse glycemic events can provide sufficient time
windows to issue appropriate responses and adjust the
therapy. Consequently, patients could avoid
hyperglycemia and hypoglycemia conditions which would
improve overall health, safety, and the quality of life
of insulin dependent patients. This report concerns to
the application of a search-based algorithm to generate
models able to capture the dynamics of the blood
glucose at a personalized patient level. The
grammar-based feature generation allows to build
complex empirical models using the data gathered by a
sensor augmented therapy, a fitness band and a basic
knowledge of T1D dynamics. Final model solutions
provide blood glucose levels estimations using
prediction horizons of 30, 60 and 90 minutes.",
notes = "co-located with the 27th International Joint
Conference on Artificial Intelligence and the 23rd
European Conference on Artificial Intelligence
(IJCAI-ECAI 2018)