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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:

An imbalance-aware deep neural network for early prediction of preeclampsia
R. Avery Bennett, Zuber D. Mulla, Pavan Parikh, et al.
PLoS ONE (2022) Vol. 17, Iss. 4, pp. e0266042-e0266042
Open Access | Times Cited: 17

Showing 17 citing articles:

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: 25
A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery
Ishfaq Yaseen, Riyaz Ahmad Rather
International Journal of Women s Health (2024) Vol. Volume 16, pp. 903-915
Open Access | Times Cited: 10
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
Artificial Intelligence and Machine Learning in Preeclampsia
Anita T. Layton
Arteriosclerosis Thrombosis and Vascular Biology (2025)
Closed Access | Times Cited: 1
Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review
Sofonyas Abebaw Tiruneh, Tra Thuan Thanh Vu, Daniel L. Rolnik, et al.
Current Hypertension Reports (2024) Vol. 26, Iss. 7, pp. 309-323
Open Access | Times Cited: 7
Diagnosis and Detection of Congenital Diseases in New-Borns or Fetuses Using Artificial Intelligence Techniques: A Systematic Review
Komalpreet Kaur, Charanjit Singh, Yogesh Kumar
Archives of Computational Methods in Engineering (2023) Vol. 30, Iss. 5, pp. 3031-3058
Closed Access | Times Cited: 16
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
An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine Learning
Braden W Eberhard, Raphael Y. Cohen, John Rigoni, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 6
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
Tomas M. Bosschieter, Zifei Xu, Hui Lan, et al.
Journal of Healthcare Informatics Research (2023)
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
Preeclampsia Susceptibility Assessment Based on Deep Learning Modeling and Single Nucleotide Polymorphism Analysis
Aida Saadaty, Sara Parhoudeh, Khalil Khashei Varnamkhasti, et al.
Biomedicines (2023) Vol. 11, Iss. 5, pp. 1257-1257
Open Access | Times Cited: 2
Derin Öğrenme ile Anne Sağlığı Risk Analizi Yapılması
Burçin Yönel Önem, Hacer Karacan
Orclever Proceedings of Research and Development (2024) Vol. 4, Iss. 1, pp. 1-18
Open Access
A Prospective Study on Risk Prediction of Preeclampsia Using Bi-Platform Calibration and Machine Learning
Zhiguo Zhao, Jiaxin Dai, Hongyan Chen, et al.
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 19, pp. 10684-10684
Open Access
A Review on Machine Learning Deployment Patterns and Key Features in the Prediction of Preeclampsia
L. B. Pedersen, Magdalena Mazur-Milecka, Jacek Rumiński, et al.
Machine Learning and Knowledge Extraction (2024) Vol. 6, Iss. 4, pp. 2515-2569
Open Access
Preeclampsia prediction via machine learning: a systematic literature review
Mert Özcan, Serhat Peker
Health Systems (2024), pp. 1-15
Closed Access
Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data
Magdalena Mazur-Milecka, Natalia Kowalczyk, Kinga Jaguszewska, et al.
Lecture notes in networks and systems (2023), pp. 267-281
Closed Access
Prediction of Hypertensive Disorders in Pregnancy using Machine Learning Algorithms
Seeta Devi, Shivali Amit Wagle
2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (2023), pp. 1-6
Closed Access
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