IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i20p3741-d940172.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/20/3741/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/20/3741/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexis Alonso-Bastida & Manuel Adam-Medina & Rubén Posada-Gómez & Dolores Azucena Salazar-Piña & Gloria-Lilia Osorio-Gordillo & Luis Gerardo Vela-Valdés, 2022. "Dynamic of Glucose Homeostasis in Virtual Patients: A Comparison between Different Behaviors," IJERPH, MDPI, vol. 19(2), pages 1-19, January.
    2. Shu-Rong Yan & Khalid A. Alattas & Mohsen Bakouri & Abdullah K. Alanazi & Ardashir Mohammadzadeh & Saleh Mobayen & Anton Zhilenkov & Wei Guo, 2022. "Generalized Type-2 Fuzzy Control for Type-I Diabetes: Analytical Robust System," Mathematics, MDPI, vol. 10(5), pages 1-20, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3741-:d:940172. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.