MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women
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 co...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-34126-7 |
| _version_ | 1827956859006353408 |
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| author | Héctor Gallardo-Rincón María Jesús Ríos-Blancas Janinne Ortega-Montiel Alejandra Montoya Luis Alberto Martinez-Juarez Julieta Lomelín-Gascón Rodrigo Saucedo-Martínez Ricardo Mújica-Rosales Victoria Galicia-Hernández Linda Morales-Juárez Lucía Marcela Illescas-Correa Ixel Lorena Ruiz-Cabrera Daniel Alberto Díaz-Martínez Francisco Javier Magos-Vázquez Edwin Oswaldo Vargas Ávila Alejandro Efraín Benitez-Herrera Diana Reyes-Gómez María Concepción Carmona-Ramos Laura Hernández-González Oscar Romero-Islas Enrique Reyes Muñoz Roberto Tapia-Conyer |
| author_facet | Héctor Gallardo-Rincón María Jesús Ríos-Blancas Janinne Ortega-Montiel Alejandra Montoya Luis Alberto Martinez-Juarez Julieta Lomelín-Gascón Rodrigo Saucedo-Martínez Ricardo Mújica-Rosales Victoria Galicia-Hernández Linda Morales-Juárez Lucía Marcela Illescas-Correa Ixel Lorena Ruiz-Cabrera Daniel Alberto Díaz-Martínez Francisco Javier Magos-Vázquez Edwin Oswaldo Vargas Ávila Alejandro Efraín Benitez-Herrera Diana Reyes-Gómez María Concepción Carmona-Ramos Laura Hernández-González Oscar Romero-Islas Enrique Reyes Muñoz Roberto Tapia-Conyer |
| author_sort | Héctor Gallardo-Rincón |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T15:11:15Z |
| format | Article |
| id | doaj.art-ea412e30f77a487496d69ca43924194c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T15:11:15Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ea412e30f77a487496d69ca43924194c2023-04-30T11:13:37ZengNature PortfolioScientific Reports2045-23222023-04-0113111110.1038/s41598-023-34126-7MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican womenHéctor Gallardo-Rincón0María Jesús Ríos-Blancas1Janinne Ortega-Montiel2Alejandra Montoya3Luis Alberto Martinez-Juarez4Julieta Lomelín-Gascón5Rodrigo Saucedo-Martínez6Ricardo Mújica-Rosales7Victoria Galicia-Hernández8Linda Morales-Juárez9Lucía Marcela Illescas-Correa10Ixel Lorena Ruiz-Cabrera11Daniel Alberto Díaz-Martínez12Francisco Javier Magos-Vázquez13Edwin Oswaldo Vargas Ávila14Alejandro Efraín Benitez-Herrera15Diana Reyes-Gómez16María Concepción Carmona-Ramos17Laura Hernández-González18Oscar Romero-Islas19Enrique Reyes Muñoz20Roberto Tapia-Conyer21University of Guadalajara, Health Sciences University CenterCarlos Slim FoundationCarlos Slim FoundationCarlos Slim FoundationCarlos Slim FoundationCarlos Slim FoundationCarlos Slim FoundationCarlos Slim FoundationCarlos Slim FoundationCarlos Slim FoundationMaternal and Childhood Research Center (CIMIGEN)Maternal and Childhood Research Center (CIMIGEN)Ministry of Health of the State of GuanajuatoMinistry of Health of the State of GuanajuatoMinistry of Health of the State of GuanajuatoMinistry of Health of the State of Hidalgo, Fraccionamiento Puerta de HierroMinistry of Health of the State of Hidalgo, Fraccionamiento Puerta de HierroMinistry of Health of the State of Hidalgo, Fraccionamiento Puerta de HierroMinistry of Health of the State of Hidalgo, Fraccionamiento Puerta de HierroMinistry of Health of the State of Hidalgo, Fraccionamiento Puerta de HierroDepartment of Endocrinology, National Institute of PerinatologySchool of Medicine, National Autonomous University of MexicoAbstract 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.https://doi.org/10.1038/s41598-023-34126-7 |
| spellingShingle | Héctor Gallardo-Rincón María Jesús Ríos-Blancas Janinne Ortega-Montiel Alejandra Montoya Luis Alberto Martinez-Juarez Julieta Lomelín-Gascón Rodrigo Saucedo-Martínez Ricardo Mújica-Rosales Victoria Galicia-Hernández Linda Morales-Juárez Lucía Marcela Illescas-Correa Ixel Lorena Ruiz-Cabrera Daniel Alberto Díaz-Martínez Francisco Javier Magos-Vázquez Edwin Oswaldo Vargas Ávila Alejandro Efraín Benitez-Herrera Diana Reyes-Gómez María Concepción Carmona-Ramos Laura Hernández-González Oscar Romero-Islas Enrique Reyes Muñoz Roberto Tapia-Conyer MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women Scientific Reports |
| title | MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women |
| title_full | MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women |
| title_fullStr | MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women |
| title_full_unstemmed | MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women |
| title_short | MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women |
| title_sort | mido gdm an innovative artificial intelligence based prediction model for the development of gestational diabetes in mexican women |
| url | https://doi.org/10.1038/s41598-023-34126-7 |
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