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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:

Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data
Shilong Li, Zichen Wang, Luciana Vieira, et al.
npj Digital Medicine (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

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: 28
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
An interpretable machine learning approach for predicting 30-day readmission after stroke
Ji Lv, Mengmeng Zhang, Yujie Fu, et al.
International Journal of Medical Informatics (2023) Vol. 174, pp. 105050-105050
Closed Access | Times Cited: 13
Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study
Gui‐You Yang, Tünde Montgomery-Csobán, Wessel Ganzevoort, et al.
PLoS Medicine (2025) Vol. 22, Iss. 2, pp. e1004509-e1004509
Open Access
Impact of antenatal SARS-CoV-2 infection on development of hypertensive disorders of pregnancy in a large, diverse, cohort
Alexandra N. Mills, Bethany Dubois, Corina Lesseur, et al.
Pregnancy Hypertension (2025) Vol. 39, pp. 101205-101205
Closed Access
A Bayesian Network model of pregnancy outcomes for England and Wales
Scott McLachlan, Bridget J Daley, Samir Saidi, et al.
Computers in Biology and Medicine (2025) Vol. 189, pp. 110026-110026
Closed Access
Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review
Sadikul Islam, Rifat Shahriyar, Abhishek Agarwala, et al.
BMC Medical Informatics and Decision Making (2025) Vol. 25, Iss. 1
Open Access
Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy
Gabriel Cubillos, Max Mönckeberg, Alejandra Plaza, et al.
BMC Pregnancy and Childbirth (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 12
AI-based preeclampsia detection and prediction with electrocardiogram data
Liam Butler, Fatma Güntürkün, Lokesh Chinthala, et al.
Frontiers in Cardiovascular Medicine (2024) Vol. 11
Open Access | Times Cited: 4
Machine learning applied to digital phenotyping: A systematic literature review and taxonomy
Marília Pit dos Santos, Wesllei Felipe Heckler, Rodrigo Simon Bavaresco, et al.
Computers in Human Behavior (2024) Vol. 161, pp. 108422-108422
Closed Access | Times Cited: 4
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
Early Pregnancy Systolic Blood Pressure Patterns Predict Early‐ and Later‐Onset Preeclampsia and Gestational Hypertension Among Ostensibly Low‐to‐Moderate Risk Groups
Erica P. Gunderson, Mara Greenberg, Baiyang Sun, et al.
Journal of the American Heart Association (2023) Vol. 12, Iss. 15
Open Access | Times Cited: 9
Forecasting Maternal Women's Health Risks using Random Forest Classifier
Dhruvi Thakkar, Vaibhav Gandhi, Dhriti Trivedi
2022 International Conference on Inventive Computation Technologies (ICICT) (2024), pp. 961-965
Closed Access | Times Cited: 2
Development of a predictive model for pediatric atopic dermatitis: A retrospective cross-sectional nationwide database study
Tamar Landau, Keren Gamrasni, Alex Levin, et al.
Annals of Allergy Asthma & Immunology (2024) Vol. 133, Iss. 3, pp. 325-334.e5
Closed 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
The Pulse of Artificial Intelligence in Cardiology: A Comprehensive Evaluation of State-of-the-art Large Language Models for Potential Use in Clinical Cardiology
Andrej Novák, Fran Rode, Ante Lisičić, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 5
Approach and Method for Bayesian Network Modelling: A Case Study in Pregnancy Outcomes for England and Wales
Scott McLachlan, Bridget J Daley, Sam Saidi, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Hypertension in Pregnancy: Current Challenges and Future Opportunities for Surveillance and Research
Elena V. Kuklina, Robert Merritt, Janet S. Wright, et al.
Journal of Women s Health (2024) Vol. 33, Iss. 5, pp. 553-562
Open Access | Times Cited: 1
Early prediction of preeclampsia using the first trimester vaginal microbiome
William F. Kindschuh, George I. Austin, Yoli Meydan, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
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
An algorithm to identify patients with rare genetic disorders and its real-world data application
Bryn D. Webb, Lisa Y. Lau, Despina Tsevdos, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk Scores
Vesela Kovacheva, Braden W Eberhard, Raphael Y. Cohen, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
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
Deep Survival Analysis for Interpretable Time-Varying Prediction of Preeclampsia Risk
Braden W Eberhard, Kathryn J. Gray, David W. Bates, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Maternal Health Risk Prediction with Machine Learning Methods
M. Ferni Ukrit, R. Beaulah Jeyavathana, Aluru Leela Rani, et al.
(2024), pp. 1-9
Closed Access
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