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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 1-25 of 58 citing articles:

Preeclampsia pathophysiology and adverse outcomes during pregnancy and postpartum
Courtney Bisson, Sydney Dautel, Easha Patel, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 61
Prediction of Preeclampsia Using Machine Learning and Deep Learning Models: A Review
Sumayh S. Aljameel, Manar Alzahrani, Reem Almusharraf, et al.
Big Data and Cognitive Computing (2023) Vol. 7, Iss. 1, pp. 32-32
Open Access | Times Cited: 24
The role of cell-free DNA biomarkers and patient data in the early prediction of preeclampsia: an artificial intelligence model
Asma Khalil, Giovanni Bellesia, Mary E. Norton, et al.
American Journal of Obstetrics and Gynecology (2024) Vol. 231, Iss. 5, pp. 554.e1-554.e18
Closed Access | Times Cited: 9
Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China
Mengyuan Liu, Xiaofeng Yang, Guolu Chen, et al.
Frontiers in Physiology (2022) Vol. 13
Open Access | Times Cited: 30
New advances in prediction and surveillance of preeclampsia: role of machine learning approaches and remote monitoring
Max Hackelöer, Leon Schmidt, Stefan Verlohren
Archives of Gynecology and Obstetrics (2022) Vol. 308, Iss. 6, pp. 1663-1677
Open Access | Times Cited: 29
Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies
Vesela Kovacheva, Braden W Eberhard, Raphael Y. Cohen, et al.
Hypertension (2023) Vol. 81, Iss. 2, pp. 264-272
Open Access | Times Cited: 21
Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study
Alina-Sînziana Melinte-Popescu, Ingrid-Andrada Vasilache, Demetra Socolov, et al.
Journal of Clinical Medicine (2023) Vol. 12, Iss. 2, pp. 418-418
Open Access | Times Cited: 20
Predicting intensive care need in women with preeclampsia using machine learning – a pilot study
Camilla Edvinsson, Ola Björnsson, Lena Erlandsson, et al.
Hypertension in Pregnancy (2024) Vol. 43, Iss. 1
Open Access | Times Cited: 6
Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study
Marian Melinte-Popescu, Ingrid-Andrada Vasilache, Demetra Socolov, et al.
Diagnostics (2023) Vol. 13, Iss. 2, pp. 287-287
Open Access | Times Cited: 15
The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare
Mohanad Alkhodari, Zhaohan Xiong, Ahsan H. Khandoker, et al.
Expert Review of Cardiovascular Therapy (2023) Vol. 21, Iss. 7, pp. 531-543
Closed Access | Times Cited: 13
An early screening model for preeclampsia: utilizing zero-cost maternal predictors exclusively
Lei Wang, Yinyao Ma, Wenshuai Bi, et al.
Hypertension Research (2024) Vol. 47, Iss. 4, pp. 1051-1062
Open Access | Times Cited: 5
Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study
Tünde Montgomery-Csobán, Kimberley Kavanagh, Paul Murray, et al.
The Lancet Digital Health (2024) Vol. 6, Iss. 4, pp. e238-e250
Open Access | Times Cited: 5
Leveraging Machine Learning to Predict and Assess Disparities in Severe Maternal Morbidity in Maryland
Qingfeng Li, Y. Natalia Alfonso, Carrie Wolfson, et al.
Healthcare (2025) Vol. 13, Iss. 3, pp. 284-284
Open Access
Artificial intelligence-based prediction of second stage duration in labor: a multicenter retrospective cohort analysis
Xiaoqing Huang, Di Xia, Suiwen Lin, et al.
EClinicalMedicine (2025) Vol. 80, pp. 103072-103072
Closed 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.
BMC Pregnancy and Childbirth (2025) Vol. 25, Iss. 1
Open Access
Advancing Obstetric Care Through Artificial Intelligence-Enhanced Clinical Decision Support Systems: A Systematic Review
Mohammad Ali, Selma Mohammed Abdelgadir Elhabeeb, Nesma Elsheikh, et al.
Cureus (2025)
Open 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.
BMC Pregnancy and Childbirth (2025) Vol. 25, Iss. 1
Open Access
Placental Biomarker Testing for Evaluation of Suspected Preeclampsia
Michelle Silasi, Marly Azzi, Sanela Potchileev, et al.
Clinical Chemistry (2025)
Closed Access
Predicting interval from diagnosis to delivery in preeclampsia using electronic health records
Xiaotong Yang, Hailey K Ballard, Aditya D Mahadevan, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Exploring the potential of cell-free RNA and Pyramid Scene Parsing Network for early preeclampsia screening
Zhuo Zhao, Xiaoxu Liu, Yonghui Guan, et al.
BMC Pregnancy and Childbirth (2025) Vol. 25, Iss. 1
Open Access
Künstliche (Artifizielle) Intelligenz (KI oder AI) im Ultraschall
J. Weichert, Christian Kollmann
Springer eBooks (2025), pp. 1183-1193
Closed Access
PRERISK Study: A Randomized Controlled Trial Evaluating a sFlt-1/PlGF-Based Calculator for Preeclampsia Hospitalization.
Ans C.M. Kluivers, Rugina I. Neuman, Langeza Saleh, et al.
PubMed (2025) Vol. 82, Iss. 5, pp. 827-838
Closed Access
Machine learning, advanced data analysis, and a role in pregnancy care? How can we help improve preeclampsia outcomes?
Annemarie Hennessy, Tu Hao Tran, Suraj Narayanan Sasikumar, et al.
Pregnancy Hypertension (2024) Vol. 37, pp. 101137-101137
Open Access | Times Cited: 3
Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension
Sreyoshi F. Alam, Maria Lourdes Gonzalez Suarez
Clinics and Practice (2024) Vol. 14, Iss. 4, pp. 1357-1374
Open Access | Times Cited: 2
Artificial intelligence augmented clinical decision support systems for pregnancy care: a systematic review (Preprint)
xueying Lin, Chen Liang, Jihong Liu, et al.
Journal of Medical Internet Research (2024) Vol. 26, pp. e54737-e54737
Open Access | Times Cited: 2
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