This paper is in the following e-collection/theme issue:

Machine Learning (1480) Clinical Information and Decision Making (1418)

Published on in Vol 8, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15411, first published .
Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort

Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort

Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort

Authors of this article:

Herdiantri Sufriyana1, 2 Author Orcid Image ;   Yu-Wei Wu1, 3 Author Orcid Image ;   Emily Chia-Yu Su1, 3, 4 Author Orcid Image
Article Authors Cited by (26) Tweetations (1) Metrics

Journals

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  4. Zhan M, Chen Z, Ding C, Qu Q, Wang G, Liu S, Wen F. Risk prediction for delayed clearance of high-dose methotrexate in pediatric hematological malignancies by machine learning. International Journal of Hematology 2021;114(4):483 View
  5. Lewandowska M. The Association of Familial Hypertension and Risk of Gestational Hypertension and Preeclampsia. International Journal of Environmental Research and Public Health 2021;18(13):7045 View
  6. Lee S, Nam Y, Choi E, Jung Y, Sriram V, Leiby J, Koo J, Oh I, Kim B, Kim S, Kim S, Kim G, Joo S, Shin S, Norwitz E, Park C, Jun J, Kim W, Kim D, Park J. Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning. Scientific Reports 2022;12(1) View
  7. Susanty S, Sufriyana H, Su E, Chuang Y, Rashid T. Questionnaire-free machine-learning method to predict depressive symptoms among community-dwelling older adults. PLOS ONE 2023;18(1):e0280330 View
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  9. Aljameel S, Alzahrani M, Almusharraf R, Altukhais M, Alshaia S, Sahlouli H, Aslam N, Khan I, Alabbad D, Alsumayt A. Prediction of Preeclampsia Using Machine Learning and Deep Learning Models: A Review. Big Data and Cognitive Computing 2023;7(1):32 View
  10. Kaur K, Singh C, Kumar Y. Diagnosis and Detection of Congenital Diseases in New-Borns or Fetuses Using Artificial Intelligence Techniques: A Systematic Review. Archives of Computational Methods in Engineering 2023;30(5):3031 View
  11. Flowers A, Gonzalez T, Joshi N, Eisman L, Clark E, Buttle R, Sauro E, DiPentino R, Lin Y, Wu D, Wang Y, Santiskulvong C, Tang J, Lee B, Sun T, Chan J, Wang E, Jefferies C, Lawrenson K, Zhu Y, Afshar Y, Tseng H, Williams J, Pisarska M. Sex differences in microRNA expression in first and third trimester human placenta. Biology of Reproduction 2022;106(3):551 View
  12. Gómez-Jemes L, Oprescu A, Chimenea-Toscano Á, García-Díaz L, Romero-Ternero M. Machine Learning to Predict Pre-Eclampsia and Intrauterine Growth Restriction in Pregnant Women. Electronics 2022;11(19):3240 View
  13. Zheng D, Hao X, Khan M, Wang L, Li F, Xiang N, Kang F, Hamalainen T, Cong F, Song K, Qiao C. Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study. Frontiers in Cardiovascular Medicine 2022;9 View
  14. Teng L, Mattar C, Biswas A, Hoo W, Saw S. Interpreting the role of nuchal fold for fetal growth restriction prediction using machine learning. Scientific Reports 2022;12(1) View
  15. Li Z, Xu Q, Sun G, Jia R, Yang L, Liu G, Hao D, Zhang S, Yang Y, Li X, Zhang X, Lian C. Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm. Frontiers in Physiology 2022;13 View
  16. de Santiago I, Polanski L. Data-Driven Medicine in the Diagnosis and Treatment of Infertility. Journal of Clinical Medicine 2022;11(21):6426 View
  17. Musa F, Prasad R. Predicting Preeclampsia Using Principal Component Analysis and Decision Tree Classifier. Current Women s Health Reviews 2023;20(2) View
  18. Rescinito R, Ratti M, Payedimarri A, Panella M. Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis. Healthcare 2023;11(11):1617 View
  19. Mennickent D, Rodríguez A, Opazo M, Riedel C, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano A, Guzmán-Gutiérrez E, Araya J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Frontiers in Endocrinology 2023;14 View
  20. Edvinsson C, Björnsson O, Erlandsson L, Hansson S. Predicting intensive care need in women with preeclampsia using machine learning – a pilot study. Hypertension in Pregnancy 2024;43(1) View
  21. Ponce H, Martínez-Villaseñor L, Martínez-Velasco A. Explainable artificial hydrocarbon networks classifier applied to preeclampsia. Information Sciences 2024;670:120556 View
  22. Yaseen I, Rather R. A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery. International Journal of Women's Health 2024;Volume 16:903 View
  23. Tiruneh S, Vu T, Rolnik D, Teede H, Enticott J. Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review. Current Hypertension Reports 2024;26(7):309 View
  24. Patel D, Chaudhari K, Acharya N, Shrivastava D, Muneeba S. Artificial Intelligence in Obstetrics and Gynecology: Transforming Care and Outcomes. Cureus 2024 View
  25. Zhao Z, Dai J, Chen H, Lu L, Li G, Yan H, Zhang J. A Prospective Study on Risk Prediction of Preeclampsia Using Bi-Platform Calibration and Machine Learning. International Journal of Molecular Sciences 2024;25(19):10684 View

Books/Policy Documents

  1. Hu Z, Hu R, Yan R, Mayer C, Rohling R, Singla R. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. View