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SLAS Technol. 2024, 29:100206. 10.1016\/j.slast.2024.100206","journal-title":"SLAS Technol"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1002\/ijgo.15782","article-title":"Design, construction, and validation of obstetric risk classification systems to predict intensive care unit admission","volume":"167","author":"Soares FM","year":"2024","unstructured":"Soares FM, da Rocha Carvalho Rosa LO, Cecatti JG, Luz AG, Awe OD, Laureano EE, de Carvalho Pacagnella R. Design, construction, and validation of obstetric risk classification systems to predict intensive care unit admission. 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JMIR Form Res. 2024, 8:e47372. 10.2196\/47372","journal-title":"JMIR Form Res"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1002\/uog.23739","article-title":"Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non-occiput anterior positions during labor","volume":"59","author":"Ghi T","year":"2022","unstructured":"Ghi T, Conversano F, Ramirez Zegarra R, et al.. Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non-occiput anterior positions during labor. 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Stud Health Technol Inform. 2024, 315:3-7. 10.3233\/SHTI240096","journal-title":"Stud Health Technol Inform"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1017\/thg.2024.48","article-title":"Artificial intelligence in predicting postpartum hemorrhage in twin pregnancies undergoing cesarean section","author":"Dogru S","year":"2025","unstructured":"Dogru S, Ezveci H, Akkus F, Bah\u00e7eci P, Karanfil Yaman F, Acar A. Artificial intelligence in predicting postpartum hemorrhage in twin pregnancies undergoing cesarean section. Twin Res Hum Genet. 2025, 1-7. 10.1017\/thg.2024.48","journal-title":"Twin Res Hum Genet"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.2217\/bmm-2023-0051","article-title":"Machine learning algorithms and computational validation of single-nucleotide polymorphisms of antioxidant enzymes and oxidative stress markers in neonates","volume":"17","author":"Sridharan K","year":"2023","unstructured":"Sridharan K, Sekaran K, Doss C GP, Jufairi MA. 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