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Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models

Author

Listed:
  • Omer Mujahid

    (Modeling, Identification and Control Laboratory, Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain)

  • Ivan Contreras

    (Modeling, Identification and Control Laboratory, Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain
    Professor Serra Húnter.)

  • Aleix Beneyto

    (Modeling, Identification and Control Laboratory, Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain)

  • Ignacio Conget

    (Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 17003 Girona, Spain
    Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08023 Barcelona, Spain
    Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic, 08036 Barcelona, Spain)

  • Marga Giménez

    (Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 17003 Girona, Spain
    Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08023 Barcelona, Spain
    Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic, 08036 Barcelona, Spain)

  • Josep Vehi

    (Modeling, Identification and Control Laboratory, Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain
    Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 17003 Girona, Spain)

Abstract

Mathematical modeling of the glucose–insulin system forms the core of simulators in the field of glucose metabolism. The complexity of human biological systems makes it a challenging task for the physiological models to encompass the entirety of such systems. Even though modern diabetes simulators perform a respectable task of simulating the glucose–insulin action, they are unable to estimate various phenomena affecting the glycemic profile of an individual such as glycemic disturbances and patient behavior. This research work presents a potential solution to this problem by proposing a method for the generation of blood glucose values conditioned on plasma insulin approximation of type 1 diabetes patients using a pixel-to-pixel generative adversarial network. Two type-1 diabetes cohorts comprising 29 and 6 patients, respectively, are used to train the generative model. This study shows that the generated blood glucose values are statistically similar to the real blood glucose values, mimicking the time-in-range results for each of the standard blood glucose ranges in type 1 diabetes management and obtaining similar means and variability outcomes. Furthermore, the causal relationship between the plasma insulin values and the generated blood glucose conforms to the same relationship observed in real patients. These results herald the aptness of deep generative models for the generation of virtual patients with diabetes.

Suggested Citation

  • Omer Mujahid & Ivan Contreras & Aleix Beneyto & Ignacio Conget & Marga Giménez & Josep Vehi, 2022. "Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3741-:d:940172
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    References listed on IDEAS

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    1. Sayyar Ahmad & Charrise M. Ramkissoon & Aleix Beneyto & Ignacio Conget & Marga Giménez & Josep Vehi, 2021. "Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts," Mathematics, MDPI, vol. 9(11), pages 1-15, May.
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    Cited by:

    1. Vincent O. Omwenga & Vaishnav Madhumati & Kumar Vinay & Sathyanarayan Srikanta & Navakanta Bhat, 2023. "Mathematical Modelling of Combined Intervention Strategies for the Management and Control of Plasma Glucose of a Diabetes Mellitus Patient: A System Dynamic Modelling Approach," Mathematics, MDPI, vol. 11(2), pages 1-17, January.

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