- author = "Silvia {Oviedo Castillo}",
- title = "Forecasting and decision support for type 1 diabetes insulin therapy using machine learning",
- school = "Departament d'Enginyeria Electrica, Electronica i Automatica, University of Girona",
- year = "2019",
- address = "Spain",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, Blood glucose, Glucosa a la sang, Glucosa en la sangre, Type 1 diabetes, Diabetis tipus 1, Diabetes tipo 1, Machine learning, Aprenentatge automatic, Aprendizaje automatico, Postprandial hypoglycemia, Hipoglucemia postprandial, Hipoglucemia postprandial, Insuline therapy, Terapia amb insulina, Terapia con insulina, Forecasting models, Models de prediccio, Modelos de prediccion",
- timestamp = "Wed, 05 Oct 2022 13:54:38 +0200",
-
biburl = "
https://dblp.org/rec/phd/es/Castillo19.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
-
URL = "
https://www.tdx.cat/bitstream/handle/10803/667332/tsoc_20190409.pdf",
-
URL = "http://hdl.handle.net/10803/667332",
- size = "83 pages",
- abstract = "Insulin therapy for Type 1 Diabetes (T1D) has several ramifications with different degrees of automation. The advances in sensors and monitoring devices have led to an increasing availability of data. Additionally, machine learning algorithms usage has sprung, allowing the development of models for Blood Glucose (BG) forecasting with relative ease. Nevertheless, BG forecasting is still a challenging task for prediction horizons beyond 30 min and, even more so, with missing or erroneous data, which is a common burden in the field. This thesis is devoted to generate machine learning models that forecast either BG levels using regression algorithms or postprandial hypoglycemia using classification algorithms. The application of these models range from Multiple Daily Injections (MDI) therapy up to Sensor Augmented Pump (SAP) therapy.",
-
notes = "in English
Supervised by: Josep Vehi Casellas and Ivan Contreras",
