Cargando…
Prediction of gestational diabetes mellitus using machine learning from birth cohort data of the Japan Environment and Children's Study
Recently, prediction of gestational diabetes mellitus (GDM) using artificial intelligence (AI) from medical records has been reported. We aimed to evaluate GDM-predictive AI-based models using birth cohort data with a wide range of information and to explore factors contributing to GDM development....
Autores principales: | Watanabe, Masahiro, Eguchi, Akifumi, Sakurai, Kenichi, Yamamoto, Midori, Mori, Chisato |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575866/ https://www.ncbi.nlm.nih.gov/pubmed/37833313 http://dx.doi.org/10.1038/s41598-023-44313-1 |
Ejemplares similares
-
Exploration of predictive metabolic factors for gestational diabetes mellitus in Japanese women using metabolomic analysis
por: Sakurai, Kenichi, et al.
Publicado: (2018) -
Association between maternal antibiotic exposure during pregnancy and childhood obesity in the Japan Environment and Children's Study
por: Sakurai, Kenichi, et al.
Publicado: (2022) -
Neurological development in 36‐month‐old children conceived via assisted reproductive technology: The Japan Environment and Children's Study
por: Miyake, Takao, et al.
Publicado: (2022) -
Association between Total and Individual PCB Congener Levels in Maternal Serum and Birth Weight of Newborns: Results from the Chiba Study of Mother and Child Health Using Weighted Quantile Sum Regression
por: Eguchi, Akifumi, et al.
Publicado: (2022) -
Maternal antibiotic exposure and childhood allergies: The Japan Environment and Children’s Study
por: Okoshi, Kouta, et al.
Publicado: (2023)