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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes
Leon J. Schmidt, J. Rieger, Mark Neznansky, et al.
American Journal of Obstetrics and Gynecology (2022) Vol. 227, Iss. 1, pp. 77.e1-77.e30
Closed Access | Times Cited: 58

Showing 26-50 of 58 citing articles:

Prevention of Pregnancy Complications Using a Multimodal Lifestyle, Screening, and Medical Model
Jim Parker, Pierre Hofstee, Shaun P. Brennecke
Journal of Clinical Medicine (2024) Vol. 13, Iss. 15, pp. 4344-4344
Open Access | Times Cited: 2
Racial/ethnic differences in pre-pregnancy conditions and adverse maternal outcomes in the nuMoM2b cohort: A population-based cohort study
Meghan E. Meredith, Lauren N. Steimle, Kaitlyn K. Stanhope, et al.
PLoS ONE (2024) Vol. 19, Iss. 8, pp. e0306206-e0306206
Open Access | Times Cited: 2
Interpretable machine learning to predict adverse perinatal outcomes: examining marginal predictive value of risk factors during pregnancy
Sun Ju Lee, Gian-Gabriel P. Garcia, Kaitlyn K. Stanhope, et al.
American Journal of Obstetrics & Gynecology MFM (2023) Vol. 5, Iss. 10, pp. 101096-101096
Closed Access | Times Cited: 6
Longitudinal assessment of angiogenic markers in prediction of adverse outcome in women with confirmed pre‐eclampsia
Julia Binder, Pilar Palmrich, Erkan Kalafat, et al.
Ultrasound in Obstetrics and Gynecology (2023) Vol. 62, Iss. 6, pp. 843-851
Open Access | Times Cited: 4
Artificial Intelligence in Early Diagnosis of Preeclampsia
Aysel Bülez, Kemal Hansu, ES Çağan, et al.
Nigerian Journal of Clinical Practice (2024) Vol. 27, Iss. 3, pp. 383-388
Open Access | Times Cited: 1
Predicting Maternal Outcomes Using Tree-based Methods in Machine Learning
Chukwudi Obinna Nwokoro, Faith‐Michael Uzoka, Ifiok J. Udo, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
Quality Improvement in the Digital Age: The Promise of Using Informatics to Improve Obstetric Anesthesia Care
Holly B. Ende, Brian T. Bateman
Anesthesia & Analgesia (2024)
Closed Access | Times Cited: 1
Artificial Intelligence–Assisted Perfusion Density as Biomarker for Screening Diabetic Nephropathy
Xiao Xie, Wenqi Wang, Hongyan Wang, et al.
Translational Vision Science & Technology (2024) Vol. 13, Iss. 10, pp. 19-19
Open Access | Times Cited: 1
A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort
Yun Chao Lin, Daniel Mallia, Andrea O. Clark-Sevilla, et al.
BMC Pregnancy and Childbirth (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 1
Deep learning-based prognosis models accurately predict the time to delivery among preeclamptic pregnancies using electronic health record
Xiaotong Yang, Hailey K Ballard, Aditya D Mahadevan, et al.
medRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 5
Preeclampsia Predictor with Machine Learning: A Comprehensive and Bias-Free Machine Learning Pipeline
Yun Chao Lin, Daniel Mallia, Andrea O. Clark-Sevilla, et al.
medRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 5
Predicting Preeclampsia Using Principal Component Analysis and Decision Tree Classifier
Farida Musa, Rajesh Prasad
Current Women s Health Reviews (2023) Vol. 20, Iss. 2
Closed Access | Times Cited: 2
Early pregnancy associations with Gestational Diabetes: methods and cohort results of the Hoosier Moms Cohort
David M. Haas, Hani Faysal, Mitchell Grecu, et al.
North American Proceedings in Gynecology & Obstetrics (2024) Vol. 3, Iss. 3
Open Access
Machine learning for HELLP syndrome prediction: algorithms, case study and challenges
Keltoum Benlaharche, Hichem Bey Benlaharche
STUDIES IN ENGINEERING AND EXACT SCIENCES (2024) Vol. 5, Iss. 2, pp. e8237-e8237
Closed Access
Preeclampsia prediction via machine learning: a systematic literature review
Mert Özcan, Serhat Peker
Health Systems (2024), pp. 1-15
Closed Access
Trends in antenatal corticosteroid administration: did our timing improve?
Paul Rostin, Stefan Verlohren, Wolfgang Henrich, et al.
Journal of Perinatal Medicine (2024) Vol. 52, Iss. 5, pp. 501-508
Closed Access
Adverse outcomes of preeclampsia: From mother to baby, pregnancy to postpartum
Frank T. Spradley, Bhavisha Bakrania, Lana McClements, et al.
Frontiers research topics (2024)
Open Access
Pädagogische Neuausrichtung und Gestaltungspotenziale
Andreas Schönfeld
Springer eBooks (2024), pp. 27-59
Closed Access
Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: Data from the Araraquara Cohort Study
Audêncio Victor, Francielly Almeida, Sancho Pedro Xavier, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Remote Maternity Care: Fernüberwachung bei Risikoschwangerschaften
Max Hackelöer, Stefan Verlohren
Die Gynäkologie (2024) Vol. 57, Iss. 12, pp. 811-818
Closed Access
Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters
Seyedeh Somayyeh Mousavi, Kim Tierney, Chad Robichaux, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios
Raquel M. Zimmerman, Edgar J. Hernández, Mark Yandell, et al.
Research Square (Research Square) (2024)
Open Access
Diagnostic value of combined maternal cardiac work and fetoplacental circulation parameters in detecting fetal intrauterine hypoxia in preeclamptic women
Juan Du, Jing Zhao, Wanyan LI, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Vegetation height estimation based on machine learning model driven by multi-source data in Eurasian temperate grassland
Wuhua Wang, Jiakui Tang, Na Zhang, et al.
Ecological Indicators (2024) Vol. 170, pp. 113013-113013
Closed Access
A Comprehensive and Bias-Free Machine Learning Approach for Risk Prediction of Preeclampsia with Severe Features in a Nulliparous Study Cohort
Yun Chao Lin, Daniel Mallia, Andrea O. Clark-Sevilla, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 1

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