Model Identification with Incomplete Input Data in Type 1 Diabetes*

التفاصيل البيبلوغرافية
العنوان: Model Identification with Incomplete Input Data in Type 1 Diabetes*
المؤلفون: Ozaslan, Basak, Aiello, Eleonora M., III, Francis J. Doyle, Dassau, Eyal
المساهمون: Ozaslan, Basak, Aiello, Eleonora M., Iii, Francis J. Doyle, Dassau, Eyal
بيانات النشر: Elsevier
Amsterdam
سنة النشر: 2023
المجموعة: Università degli Studi di Trento: CINECA IRIS
مصطلحات موضوعية: Identification and validation, Quantification of physiological parameter, Grey box modeling, Physiological ModelType 1 Diabetes
الوصف: A major challenge in fitting models to glucose metabolism in people with type 1 diabetes is incomplete data as its collection partially relies on self-reporting and does not include all relevant events. We develop a method for identifying optimal input corrections to reestablish a correct input-output relationship in the data while jointly identifying personalized model parameters. The unreported or misreported parts in the data are reconciled by adding sparse corrections via mixed-integer quadratic programming leading to an improved identification of the model parameters. We conduct numerical experiments with incomplete in-silico training data and show that models obtained from our method are able to provide more accurate predictions on test data than models obtained from standard methods. The performance of our methodology is similar to that attained with the standard method when trained on data with complete information.
نوع الوثيقة: conference object
اللغة: English
العلاقة: ispartofbook:22nd IFAC World Congress; IFAC WC 2023; volume:56; issue:2; firstpage:6518; lastpage:6524; numberofpages:7; serie:IFAC-PAPERSONLINE; https://hdl.handle.net/11572/402010Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85184960474; https://www.sciencedirect.com/science/article/pii/S2405896323006547?via=ihubTest
DOI: 10.1016/j.ifacol.2023.10.299
الإتاحة: https://doi.org/10.1016/j.ifacol.2023.10.299Test
https://hdl.handle.net/11572/402010Test
https://www.sciencedirect.com/science/article/pii/S2405896323006547?via=ihubTest
حقوق: info:eu-repo/semantics/openAccess
رقم الانضمام: edsbas.39AC8FC3
قاعدة البيانات: BASE