Interpretable machine learning to predict adverse perinatal outcomes: examining marginal predictive value of risk factors during pregnancy-Reference-Cited by-同舟云学术

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Interpretable machine learning to predict adverse perinatal outcomes: examining marginal predictive value of risk factors during pregnancy

Published:2023-10 Issue:10 Volume:5 Page:101096
ISSN:2589-9333
Container-title:American Journal of Obstetrics & Gynecology MFM
language:en
Short-container-title:American Journal of Obstetrics & Gynecology MFM

Author:

Lee Sun Ju^ORCID,Garcia Gian-Gabriel P.,Stanhope Kaitlyn K.,Platner Marissa H.,Boulet Sheree L.

Funder

Georgia CTSA

Publisher

Elsevier BV

Subject

General Medicine,Obstetrics and Gynecology

Reference53 articles.

1. Decline in perinatal mortality in the United States, 2017-2019;Valenzuela;NCHS Data Brief,2022

2. Mortality in the United States, 2019;Kochanek;NCHS Data Brief,2020

3. Prevention of preterm birth: harnessing science to address the global epidemic;Rubens;Sci Transl Med,2014

4. Working to address the tragedy of stillbirth,2023

5. Deep learning-a technology with the potential to transform health care;Hinton;JAMA,2018

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Bibliometric Analysis of Intelligent Techniques for Obstetric Complication Prediction in the Last 20 Years;2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA);2024-08-06

2. Strategies for Safeguarding High-Risk Pregnancies From Preterm Birth: A Narrative Review;Cureus;2024-03-07

3. Innovative Models for Integrative Prenatal Care;BRAIN. Broad Research in Artificial Intelligence and Neuroscience;2024-02-06

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