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The original multicenter prospective cohort study, 'Guido mi embarazo,' was funded by Global Health Partnership Eli Lilly and Company. The funder was not involved in the study design; collection, analysis, or interpretation of data; writing of the report; or the decision to submit this paper for publication.

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Article

MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women

Publicated to:Scientific Reports. 13 (1): 6992- - 2023-04-28 13(1), DOI: 10.1038/s41598-023-34126-7

Authors: Gallardo-Rincón, H.; Ríos-Blancas, M.J.; Ortega-Montiel, J.; Montoya, A.; Martínez-Juárez, L.A.; Lomelín-Gascón, J.; Saucedo-Martínez, R.; Mújica-Rosales, R.; Galicia-Hernández, V.; Morales-Juárez, L.; Illescas-Correa, L.M.; Ruiz-Cabrera, I.L.; Díaz-Martínez, D.A.; Magos-Vázquez, F.J.; Ávila, E.O.V.; Benítez-Herrera, A.E.; Reyes-Gómez, D.; Carmona-Ramos, M.C.;...

Affiliations

Carlos Slim Fdn, Lago Zurich 245,Presa Falcon Bldg Floor 20,Col Amp, Mexico City 11529, Mexico - Author
Maternal & Childhood Res Ctr CIMIGEN, Tlahuac 1004, Mexico City 09890, Mexico - Author
Minist Hlth State Guanajuato, Tamazuca 4, Guanajuato 36000, Gto, Mexico - Author
Minist Hlth State Hidalgo, Fraccionamiento Puerta Hierro, Ave Min 103, Pachuca 42080, Hidalgo, Mexico - Author
Natl Inst Perinatol, Dept Endocrinol, Montes Urales 800, Mexico City 11000, Mexico - Author
Natl Inst Publ Hlth, Univ 655, Cuernavaca 62100, Mexico - Author
Univ Guadalajara, Hlth Sci Univ Ctr, Guadalajara 44340, Jalisco, Mexico - Author
Univ Nacl Autonoma Mexico, Sch Med, Univ 3004, Mexico City 04510, Mexico - Author
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Abstract

Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who participated in the multicenter prospective cohort study 'Cuido mi embarazo'. A machine-learning-driven method was used to select the best predictive variables for GDM risk: age, family history of type 2 diabetes, previous diagnosis of hypertension, pregestational body mass index, gestational week, parity, birth weight of last child, and random capillary glucose. An artificial neural network approach was then used to build the model, which achieved a high level of accuracy (70.3%) and sensitivity (83.3%) for identifying women at high risk of developing GDM. This AI-based model will be applied throughout Mexico to improve the timing and quality of GDM interventions. Given the ease of obtaining the model variables, this model is expected to be clinically strategic, allowing prioritization of preventative treatment and promising a paradigm shift in prevention and primary healthcare during pregnancy. This AI model uses variables that are easily collected to identify pregnant women at risk of developing GDM with a high level of accuracy and precision.

Keywords
DiagnosisFasting plasma-glucoseInternational associationIntrauterine exposureLife-styleMellitusMiddle-income countriesNeural-networksPregnancyRisk stratification

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Scientific Reports due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 25/134, thus managing to position itself as a Q1 (Primer Cuartil), in the category Multidisciplinary Sciences.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 17.51, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions May 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-05-18, the following number of citations:

  • Scopus: 11
  • OpenCitations: 10
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-18:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 137.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 136 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 4.85.
  • The number of mentions on the social network X (formerly Twitter): 7 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Mexico.