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Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis

Author

Listed:
  • Alvis Cabrera

    (Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain)

  • Lyvia Biagi

    (Campus Guarapuava, Federal University of Technology–Paraná (UTFPR), Guarapuava 85053-525, Brazil)

  • Aleix Beneyto

    (Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain)

  • Ernesto Estremera

    (Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain)

  • Iván Contreras

    (Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain)

  • Marga Giménez

    (Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
    Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain)

  • Ignacio Conget

    (Diabetes Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
    Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain)

  • Jorge Bondia

    (Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain
    Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain)

  • Josep Antoni Martín-Fernández

    (Department of Computer Science, Applied Mathematics and Statistics, University of Girona, 17003 Girona, Spain)

  • Josep Vehí

    (Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17003 Girona, Spain
    Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain)

Abstract

Glycemia assessment in people with type 1 diabetes (T1D) has focused on the time spent in different glucose ranges. As this time reflects the relative contributions to the finite duration of a day, it should be treated as compositional data (CoDa) that can be applied to T1D data. Previous works presented a tool for the individual categorization of days and proposed a probabilistic transition model between categories, although validation has hitherto not been presented. In this study, we consider data from eight real adult patients with T1D obtained from continuous glucose monitoring (CGM) sensors and introduce a methodology based on compositional methods to validate the previously presented probability transition model. We conducted 5-fold cross-validation, with both the training and validation data being CoDa vectors, which requires developing new performance metrics. We design new accuracy and precision measures based on statistical error calculations. The results show that the precision for the entire model is higher than 95% in all patients. The use of a probabilistic transition model can help doctors and patients in diabetes treatment management and decision-making. Although the proposed method was tested with CoDa applied to T1D data obtained from CGM, the newly developed accuracy and precision measures apply to any other data or validation based on CoDa.

Suggested Citation

  • Alvis Cabrera & Lyvia Biagi & Aleix Beneyto & Ernesto Estremera & Iván Contreras & Marga Giménez & Ignacio Conget & Jorge Bondia & Josep Antoni Martín-Fernández & Josep Vehí, 2023. "Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis," Mathematics, MDPI, vol. 11(5), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1241-:d:1087580
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    References listed on IDEAS

    as
    1. John Aitchison & Michael Greenacre, 2002. "Biplots of compositional data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 375-392, October.
    2. Juan José Egozcue & Vera Pawlowsky-Glahn, 2019. "Compositional data: the sample space and its structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 599-638, September.
    3. Mayer, D. G. & Stuart, M. A. & Swain, A. J., 1994. "Regression of real-world data on model output: An appropriate overall test of validity," Agricultural Systems, Elsevier, vol. 45(1), pages 93-104.
    4. Juan José Egozcue & Vera Pawlowsky-Glahn, 2019. "Rejoinder on: Compositional data: the sample space and its structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 658-663, September.
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    Cited by:

    1. Alvis Cabrera & Ernesto Estremera & Aleix Beneyto & Lyvia Biagi & Iván Contreras & Josep Antoni Martín-Fernández & Josep Vehí, 2023. "Individualized Prediction of Blood Glucose Outcomes Using Compositional Data Analysis," Mathematics, MDPI, vol. 11(21), pages 1-17, November.

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