Swarm hybrid optimization for a piecewise model fitting applied to a glucose model

Acedo, L.; Botella, Marta; López, Juan Carlos Cortés; Hidalgo, J. Ignacio; Maqueda, Esther; Villanueva, Rafael

doi:10.1108/jsit-10-2017-0103

Cited by 8 publications

(17 citation statements)

References 25 publications

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“…In this section, we present the model we work with to describe the glucose behavior over the time of a type 2 diabetic patient. It is an adaptation of the one presented in the work of Bock et al described in the work of Acedo et al, and it is based on a system of five difference equations:

U_{t + 1} = U_{t} + V_{t},

V_{t + 1} = V_{t} - 2 a_{g} V_{t} - a_{g}^{2} U_{t} + K_{g} a_{g}^{2} C h_{t},

G_{t + 1} = G_{t} - X_{t} G_{t} - S_{g_{0}} G_{t} + U_{endo} + C \frac{U_{t}}{M},

X_{t + 1} = X_{t} - a_{x} X_{t} + a_{x} X_{t}^{1},

X_{t + 1}^{1} = X_{t}^{1} - a_{x} X_{t}^{1} + K_{x} a_{x} \frac{I_{t}}{M},

where U t represents the ...…”

Section: Model Descriptionmentioning

confidence: 99%

“…In addition, a g is the inverse of the meal time constant, K g is the unitless bioavalibility of the meal of interest, S g 0 is the glucose effectiveness at zero insulin, U endo is the insulin independent endogenous glucose production, a x is the inverse of the insulin absorption/action time constant, and K x is the insulin sensitivity. 8 In Table 1, we have collected the units of the model variables, parameters, and constants.…”

Section: Model Descriptionmentioning

confidence: 99%

“…In this section, we present the model we work with to describe the glucose behavior over the time of a type 2 diabetic patient. It is an adaptation of the one presented in the work of Bock et al 7 described in the work of Acedo et al, 8 and it is based on a system of five difference equations:…”

Section: Model Descriptionmentioning

confidence: 99%

“…This model has been also applied to study the evolution of the levels of glucose in particular patients. 8 Then, taking 30 minutes data of the patient's levels of glucose, we calibrate the model considering the data uncertainty. With the model parameters obtained in the calibration, we predict the levels of glucose over the next 15 minutes.…”

Section: Introductionmentioning

confidence: 99%

“…Although the work of Bock et al presents a minimal model, we are able to characterize it for each patient, tuning the model parameter values, that are associated to values used in daily therapy. This model has been also applied to study the evolution of the levels of glucose in particular patients …”

Section: Introductionmentioning

confidence: 99%

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A computational technique to predict the level of glucose of a diabetic patient with uncertainty in the short term

Burgos

Cervigón

Hidalgo

et al. 2019

Comp and Math Methods

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On advanced stages of the disease, diabetic patients have to inject insulin doses to maintain blood glucose levels inside of a healthy range. The decision of how much insulin is injected implies somehow to predict the level of glucose they will have after a certain time. Due to the sudden changes in the glucose levels, their estimation is a very difficult task. If we were able to give reliable estimations in advance, it would facilitate the process of taking therapeutic decisions to control the disease and improve the health of the patient. In this work, we present a technique to estimate the glucose level of a diabetic patient, capturing the measurement errors produced by continuous glucose monitoring systems (CGMSs), smart devices that measure glucose levels. To do that, we will use a model of glucose dynamics and we calibrate it with the aim to capture the glucose level data of the patient in a time interval of 30 minutes and the uncertainty given by the glucose measurement. Then, we use the calibrated parameters to predict the levels of glucose over the next 15 minutes. Repeating this procedure every 15 minutes, we are able to give short‐term accurate predictions.

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U_{t + 1} = U_{t} + V_{t},

V_{t + 1} = V_{t} - 2 a_{g} V_{t} - a_{g}^{2} U_{t} + K_{g} a_{g}^{2} C h_{t},

G_{t + 1} = G_{t} - X_{t} G_{t} - S_{g_{0}} G_{t} + U_{endo} + C \frac{U_{t}}{M},

X_{t + 1} = X_{t} - a_{x} X_{t} + a_{x} X_{t}^{1},

X_{t + 1}^{1} = X_{t}^{1} - a_{x} X_{t}^{1} + K_{x} a_{x} \frac{I_{t}}{M},

where U t represents the ...…”

Section: Model Descriptionmentioning

confidence: 99%

Section: Model Descriptionmentioning

confidence: 99%

“…In this section, we present the model we work with to describe the glucose behavior over the time of a type 2 diabetic patient. It is an adaptation of the one presented in the work of Bock et al 7 described in the work of Acedo et al, 8 and it is based on a system of five difference equations:…”

Section: Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations