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Journal Abstract Search

224 related items for PubMed ID: 36003638

  • 1. Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms.
    Bai X, Zhou Z, Su M, Li Y, Yang L, Liu K, Yang H, Zhu H, Chen S, Pan H.
    Front Public Health; 2022; 10():940182. PubMed ID: 36003638
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  • 7. [Development and evaluation of a machine learning prediction model for large for gestational age].
    Bai X, Luo YY, Zhou ZB, Su ML, Yang LQ, Chen S, Yang HB, Zhu HJ, Pan H.
    Zhonghua Liu Xing Bing Xue Za Zhi; 2021 Dec 10; 42(12):2143-2148. PubMed ID: 34954978
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  • 9. Improving preterm newborn identification in low-resource settings with machine learning.
    Rittenhouse KJ, Vwalika B, Keil A, Winston J, Stoner M, Price JT, Kapasa M, Mubambe M, Banda V, Muunga W, Stringer JSA.
    PLoS One; 2019 Dec 10; 14(2):e0198919. PubMed ID: 30811399
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  • 10. Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa.
    Sazawal S, Ryckman KK, Das S, Khanam R, Nisar I, Jasper E, Dutta A, Rahman S, Mehmood U, Bedell B, Deb S, Chowdhury NH, Barkat A, Mittal H, Ahmed S, Khalid F, Raqib R, Manu A, Yoshida S, Ilyas M, Nizar A, Ali SM, Baqui AH, Jehan F, Dhingra U, Bahl R.
    BMC Pregnancy Childbirth; 2021 Sep 07; 21(1):609. PubMed ID: 34493237
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  • 12. Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study.
    Kuhle S, Maguire B, Zhang H, Hamilton D, Allen AC, Joseph KS, Allen VM.
    BMC Pregnancy Childbirth; 2018 Aug 15; 18(1):333. PubMed ID: 30111303
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  • 13. Prediction of small for gestational age neonates: screening by maternal factors, fetal biometry, and biomarkers at 35-37 weeks' gestation.
    Ciobanu A, Rouvali A, Syngelaki A, Akolekar R, Nicolaides KH.
    Am J Obstet Gynecol; 2019 May 15; 220(5):486.e1-486.e11. PubMed ID: 30707967
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  • 14. Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery: a retrospective cohort study.
    Tong C, Du X, Chen Y, Zhang K, Shan M, Shen Z, Zhang H, Zheng J.
    Int J Surg; 2024 Apr 01; 110(4):2207-2216. PubMed ID: 38265429
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  • 15. Gradient boosted trees with individual explanations: An alternative to logistic regression for viability prediction in the first trimester of pregnancy.
    Vaulet T, Al-Memar M, Fourie H, Bobdiwala S, Saso S, Pipi M, Stalder C, Bennett P, Timmerman D, Bourne T, De Moor B.
    Comput Methods Programs Biomed; 2022 Jan 01; 213():106520. PubMed ID: 34808532
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  • 16. Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics and medical history.
    Papastefanou I, Wright D, Nicolaides KH.
    Ultrasound Obstet Gynecol; 2020 Aug 01; 56(2):196-205. PubMed ID: 32573831
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  • 18. Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm.
    Hu X, Hu X, Yu Y, Wang J.
    Front Endocrinol (Lausanne); 2023 Aug 01; 14():1105062. PubMed ID: 36967760
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  • 19. First-trimester prediction of small-for-gestational age in pregnancies at false-positive high or intermediate risk for fetal aneuploidy.
    Yarygina TA, Bataeva RS, Benitez L, Figueras F.
    Ultrasound Obstet Gynecol; 2020 Dec 01; 56(6):885-892. PubMed ID: 31909555
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