Alternative Frameworks for Personalized Insulin–Glucose Models

Kirchsteiger, Harald; Efendic, Hajrudin; Reiterer, Florian; Re, Luigi del

doi:10.1007/978-3-319-25913-0_1

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…where g u i are the impulse response coefficients of the insulin administration effect, g d i are the impulse response coefficients of the carbohydrate intake effect, and M u , M d are the assumed lengths of these impulse responses, respectively. Signal ϵ(t) ∼ N (0, σ 2 ϵ ) stands for the uncorrelated zeromean random process, which is meant to represent continuous glucose monitoring sensor noise [29], [30] combined with the effects of other exogenous unmeasurable disturbances.…”

Section: Model Structure and Preliminariesmentioning

confidence: 99%

Robust Online Correlation Method for Identification of a Nonparametric Model of Type 1 Diabetes

Dodek,

Miklovičová

2024

IEEE Access

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The paper presents an online version of the identification method for estimating the impulse responses in the case of a two-input single-output linear empirical model of type 1 diabetes that allows us to adapt the model parameters due to the intra-subject time variability in real time. The method builds on and augments our original research by providing important enhancements concerning the online parameter estimation, recursive formulation of essential equations, improved regularization, and new effective approaches to numerically solve the estimation problem. Recursive equations are derived to update the covariance matrix of the sample cross-correlation function, as well as the inverse of this covariance matrix, where the customized Sherman-Morrison formula was considered. To efficiently update the parameter estimate at each sample while avoiding direct calculation of the Hessian matrix inverse, two alternative strategies are proposed to be applied instead. The first is based on the numeric minimization by the conjugate gradient method, whereas the second takes advantage of the Schulz method to approximate the inverse Hessian matrix. As a result, all steps of the identification algorithm were designed so that only basic linear operations are required. Features to robustify the estimate were also involved, as the optimal regularization strategies based on the inverse of the covariance matrix of the actual parameter distribution and the inter-sample parameter drift were applied. In the end of the paper, a series of simulation-based experiments was carried out to assess the effectiveness of the proposed method and to demonstrate all of its aspects and important characteristics. The documented results showed that the method can yield valid estimates of impulse responses and also effectively adapt parameters in real time under the influence of time-varying physiology.

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Section: Model Structure and Preliminariesmentioning

confidence: 99%

Robust Online Correlation Method for Identification of a Nonparametric Model of Type 1 Diabetes

Dodek,

Miklovičová

2024

IEEE Access

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“…Sin embargo, este impulso, con una fuerza determinada, tiene la capacidad de desequilibrar el sistema, creando una perturbación. En este sentido, los bolos de insulina y las comidas, y sólo indicando el momento exacto en que tienen lugar, son variables de pulso y se sabe que estas características son nulas la mayor parte del tiempo [79]. Se consideran acontecimientos discretos, independientemente del tiempo que tarde en finalizar su influencia.…”

Section: Variables Relacionadas Con Dmt1 En Un Contexto Iomtunclassified

El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1

Rodríguez‐Rodríguez¹,

Rodríguez²,

Campo-Valera³

2023

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La diabetes mellitus de tipo 1 (DMT1) es una afección metabólica caracterizada por una hiperglucemia persistente como consecuencia de una síntesis pancreática insuficiente de insulina. Esto obliga a los pacientes a ser conscientes de sus oscilaciones diarias del nivel de glucosa en sangre para deducir un patrón y anticipar la glucemia futura y, por tanto, decidir la cantidad de insulina que debe inyectarse exógenamente para mantener la glucemia dentro del intervalo objetivo. Este enfoque suele adolecer de una imprecisión relativamente alta, que puede resultar peligrosa. Sin embargo, los recientes avances en las tecnologías de la información y la comunicación (TIC) y los innovadores biosensores que podrían permitir una evaluación completa y en tiempo real de la salud del paciente ofrecen un nuevo punto de vista sobre el tratamiento de la DMT1. En este sentido, las tecnologías disruptivas emergentes como Big Data, Internet de las Cosas Médicas (IoMT), Cloud Computing y Machine Learning (ML) pueden desempeñar un papel importante en la gestión de la DMT1. En este trabajo, se realiza una explicación de los enfoques basados en IoMT publicados anteriormente para la gestión de la diabetes, evaluando también los obstáculos que los futuros sistemas inteligentes de gestión IoMT deben superar. Por último, proporcionamos una visión general de una propuesta integral basada en IoMT para la gestión de la DM1 que pretende abordar los límites de los estudios anteriores y, al mismo tiempo, utilizar las tecnologías disruptivas destacadas anteriormente.

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“…However, this impulse, with a given strength, has the capability to unbalance the system, creating a disruption. In this sense, insulin boluses and meals, and just indicating the exact time when they take place, are pulse variables, and it is known that these features are zero most of the time [ 38 ]. They are considered to be discrete events, regardless of the time that it takes to end their influence.…”

Section: Significant Variables To Be Monitored For Complete Diabetmentioning

confidence: 99%

Variables to Be Monitored via Biomedical Sensors for Complete Type 1 Diabetes Mellitus Management: An Extension of the “On-Board” Concept

Rodríguez‐Rodríguez

Rodríguez

Zamora-Izquierdo

2018

Journal of Diabetes Research

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Type 1 diabetes mellitus (DM1) is a growing disease, and a deep understanding of the patient is required to prescribe the most appropriate treatment, adjusted to the patient's habits and characteristics. Before now, knowledge regarding each patient has been incomplete, discontinuous, and partial. However, the recent development of continuous glucose monitoring (CGM) and new biomedical sensors/gadgets, based on automatic continuous monitoring, offers a new perspective on DM1 management, since these innovative devices allow the collection of 24-hour biomedical data in addition to blood glucose levels. With this, it is possible to deeply characterize a diabetic person, offering a better understanding of his or her illness evolution, and, going further, develop new strategies to manage DM1. This new and global monitoring makes it possible to extend the “on-board” concept to other features. This well-known approach to the processing of variable “insulin” describes some inertias and aggregated/remaining effects. In this work, such analysis is carried out along with a thorough study of the significant variables to be taken into account/monitored—and how to arrange them—for a deep characterization of diabetic patients. Lastly, we present a case study evaluating the experience of the continuous and comprehensive monitoring of a diabetic patient, concluding that the huge potential of this new perspective could provide an acute insight into the patient's status and extract the maximum amount of knowledge, thus improving the DM1 management system in order to be fully functional.

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Alternative Frameworks for Personalized Insulin–Glucose Models

Cited by 5 publications

References 29 publications

Robust Online Correlation Method for Identification of a Nonparametric Model of Type 1 Diabetes

Robust Online Correlation Method for Identification of a Nonparametric Model of Type 1 Diabetes

El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1

Variables to Be Monitored via Biomedical Sensors for Complete Type 1 Diabetes Mellitus Management: An Extension of the “On-Board” Concept

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