A simulator with realistic and challenging scenarios for virtual T1D patients undergoing CSII and MDI therapy

Estremera, Ernesto; Cabrera, Alvis; Beneyto, Aleix; Vehı́, Josep

doi:10.1016/j.jbi.2022.104141

Cited by 7 publications

(6 citation statements)

References 53 publications

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“…Basal and bolus insulin values were first converted into the patient’s PI approximation using Hovorka’s insulin pharmacokinetic model 7 . On the other hand, carbohydrate values that were given in grams in the original dataset were depicted as the RA values using the mixed meal libraries from Ernesto et al 61 . RA values of meals that fit meal descriptions based on the time of the day the meal were considered, and the number of grams of the meal was chosen to represent a particular meal.…”

Section: Methodsmentioning

confidence: 99%

Generative deep learning for the development of a type 1 diabetes simulator

Mujahid,

Contreras,

Beneyto

et al. 2024

Commun Med

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Background Type 1 diabetes (T1D) simulators, crucial for advancing diabetes treatments, often fall short of capturing the entire complexity of the glucose-insulin system due to the imprecise approximation of the physiological models. This study introduces a simulation approach employing a conditional deep generative model. The aim is to overcome the limitations of existing T1D simulators by synthesizing virtual patients that more accurately represent the entire glucose-insulin system physiology. Methods Our methodology utilizes a sequence-to-sequence generative adversarial network to simulate virtual T1D patients causally. Causality is embedded in the model by introducing shifted input-output pairs during training, with a 90-min shift capturing the impact of input insulin and carbohydrates on blood glucose. To validate our approach, we train and evaluate the model using three distinct datasets, each consisting of 27, 12, and 10 T1D patients, respectively. In addition, we subject the trained model to further validation for closed-loop therapy, employing a state-of-the-art controller. Results The generated patients display statistical similarity to real patients when evaluated on the time-in-range results for each of the standard blood glucose ranges in T1D management along with means and variability outcomes. When tested for causality, authentic causal links are identified between the insulin, carbohydrates, and blood glucose levels of the virtual patients. The trained generative model demonstrates behaviours that are closer to reality compared to conventional T1D simulators when subjected to closed-loop insulin therapy using a state-of-the-art controller. Conclusions These results highlight our approach’s capability to accurately capture physiological dynamics and establish genuine causal relationships, holding promise for enhancing the development and evaluation of therapies in diabetes.

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Section: Methodsmentioning

confidence: 99%

Generative deep learning for the development of a type 1 diabetes simulator

Mujahid,

Contreras,

Beneyto

et al. 2024

Commun Med

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“…This study analyzed a cohort of virtual T1D patients that was generated and simulated using a modified version of a recently published realistic simulator [8]. The software application is based on the Hovorka model [9] and allows for the generation of challenging scenarios; in this study, it was used to generate models of circadian variability in insulin sensitivity and exercise, as well as a library of mixed meals.…”

Section: Methodology 21 Patient Cohort Scenario and Preprocessingmentioning

confidence: 99%

“…The experiments in this study relied on the mixed-meal library presented in [8,10]. The mixed-meal library is based on real meals, providing the possibility of experimenting under realistic conditions and parameters.…”

Section: Methodology 21 Patient Cohort Scenario and Preprocessingmentioning

confidence: 99%

“…Matlab scripts were developed to modify the T1D simulator in [8] in order to generate the simulated patient data for experimentation and to perform all data preprocessing operations. A Python-based program was developed to implement the LSTM-based machine learning model described in the previous subsection and to compute performance metrics when fed with different RA-generated curves for the simulated patients.…”

Section: Technical Specificationsmentioning

confidence: 99%

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Active Labeling Correction of Mealtimes and the Appearance of Types of Carbohydrates in Type 1 Diabetes Information Records

Contreras,

Muñoz-Organero,

Beneyto

et al. 2023

Mathematics

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People with type 1 diabetes are required to adhere to their treatment rigorously to ensure maximum benefits. Diabetes tracking tools have played an important role in this regard. Type 1 diabetes monitoring has evolved and matured with the advent of blood glucose monitor sensors, insulin pens, and insulin pump automation. However, carbohydrate monitoring has seen little progress despite carbohydrates representing a major potential disruption. Relying on the modeling of carbohydrate intake using the rate of exogenous glucose appearance, we first present a methodology capable of identifying the type of carbohydrates ingested by classifying them into fast and non-fast carbohydrates. Second, we test the ability of the methodology to identify the correct synchrony between the actual mealtime and the time labeled as such in diabetes records. A deep neural network is trained with processed input data that consist of different values to estimate the parameters in a series of experiments in which, firstly, we vary the response of ingested carbohydrates for subsequent identification and, secondly, we shift the learned carbohydrate absorption curves in time to estimate when the meals were administered to virtual patients. This study validates that the identification of different carbohydrate classes in the meal records of people with type 1 diabetes could become a valuable source of information, as it demonstrates the potential to identify inaccuracies in the recorded meal records of these patients, suggesting the potential abilities of the next generation of type 1 diabetes management tools.

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“…Other simulators have also been described and can be obtained by contacting the authors. These include a statistical virtual patient population published by Haidar et al [72], a multivariable simulator developed by Cinar and colleagues [73] that permits scheduling of exercise bouts (with intensity and duration that can be randomly modified) and also provides as outputs the values of physiological variables that are reported or predicted (energy expenditure) by wearable devices, a simulator developed by Vehi and colleagues [74], and a simulator developed by Wilinska and Hovorka [75].…”

Section: B Simulators Available To Generate Data In Diabetes Researchmentioning

confidence: 99%

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

Jacobs,

Herrero,

Facchinetti

et al. 2024

IEEE Rev. Biomed. Eng.

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Objective: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. Methods: Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an opensource library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. Significance: These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

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A simulator with realistic and challenging scenarios for virtual T1D patients undergoing CSII and MDI therapy

Cited by 7 publications

References 53 publications

Generative deep learning for the development of a type 1 diabetes simulator

Generative deep learning for the development of a type 1 diabetes simulator

Active Labeling Correction of Mealtimes and the Appearance of Types of Carbohydrates in Type 1 Diabetes Information Records

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

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