- author = "Marina {De La Cruz} and Carlos Cervigon and Jorge Alvarado and Marta Botella-Serrano and J. Ignacio Hidalgo",
- title = "Evolving Classification Rules for Predicting Hypoglycemia Events",
- booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
- year = "2022",
- editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
- address = "Padua, Italy",
- month = "18-23 " # jul,
- keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Structured Grammatical Evolution, Wearable Health Monitoring Systems, Predictive models, Prediction algorithms, Diabetes, Glucose, Grammar, Proposals, Diabetes, Hypoglycemia prediction, PWD, Rule System",
- isbn13 = "978-1-6654-6708-7",
-
DOI = "
doi:10.1109/CEC55065.2022.9870380",
- abstract = "People with diabetes have to properly manage their blood glucose levels in order to avoid acute complications. This is a difficult task and an accurate and timely prediction may be of vital importance, specially of extreme values. Perhaps one of the main concerns of people with diabetes is to suffer an hypoglycemia (low value) event and moreover, that the event will be prolonged in time. It is crucial to predict events of hyperglycemia (high value) and hypoglycemia that may cause health damages in the short term and potential permanent damages in the long term. The aim of this paper is to describe our research on predicting hypoglycemia events using Dynamic structured Grammatical Evolution. Our proposal gives white box models induced by a grammar based on if-then-else conditions. We trained and tested our system with real data collected from 5 different diabetic patients, producing 30 minutes predictions with encouraging results.",
-
notes = "Also known as \cite{9870380}
Predict human blood glucose concentration using number of steps smartwatch => energy expended by person and wearable mobile Continuous Glucose Monitoring System",
