Abstract:
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[EN] Many problems are confronted when characterizing a type 1 diabetic patient such as model mismatches, noisy inputs, measurement errors and huge variability in the glucose profiles. In this work we introduce a new ...[+]
[EN] Many problems are confronted when characterizing a type 1 diabetic patient such as model mismatches, noisy inputs, measurement errors and huge variability in the glucose profiles. In this work we introduce a new identification method based on interval analysis where variability and model imprecisions are represented by an interval model as parametric uncertainty.
The minimization of a composite cost index comprising: (1) the glucose envelope width predicted by the interval model, and (2) a Hausdorff-distance-based prediction error with respect to the envelope, is proposed. The method is evaluated with clinical data consisting in insulin and blood glucose reference measurements from 12 patients for four different lunchtime postprandial periods each.
Following a “leave-one-day-out” cross-validation study, model prediction capabilities for validation days were encouraging (medians of: relative error = 5.45%, samples predicted = 57%, prediction width = 79.1 mg/dL). The consideration of the days with maximum patient variability represented as identification days, resulted in improved prediction capabilities for the identified model (medians of: relative error = 0.03%, samples predicted = 96.8%, prediction width = 101.3 mg/dL). Feasibility of interval models identification in the context of type 1 diabetes was demonstrated.
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Thanks:
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The research leading to these results has received funding from the Spanish Ministry
of Science and Innovation under grant DPI2010-20764-C02-01, the European
Union Seventh Framework Programme (FP7/2007–2013) under grant ...[+]
The research leading to these results has received funding from the Spanish Ministry
of Science and Innovation under grant DPI2010-20764-C02-01, the European
Union Seventh Framework Programme (FP7/2007–2013) under grant agreement
FP7-PEOPLE-2009-IEF, Ref 252085 and the GeneralitatValenciana through Grant
GV/2012/085.
The authors acknowledge the collaboration of Sara Correa, Geles
Viguer and Pepa Gabaldón from the Diabetes Reference Unit in the
Clinic University Hospital of Valencia, and the selfless participation
of all the patients involved in the experiments from which data
were obtained.
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