Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study
Anna Sandström, Jonathan M. Snowden, Jonas Höijer, et al.
PLoS ONE (2019) Vol. 14, Iss. 11, pp. e0225716-e0225716
Open Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis
Herdiantri Sufriyana, Atina Husnayain, Ya-Lin Chen, et al.
JMIR Medical Informatics (2020) Vol. 8, Iss. 11, pp. e16503-e16503
Open Access | Times Cited: 91
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
Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications
Daniela Mennickent, Andrés Rodrı́guez, María Cecilia Opazo, et al.
Frontiers in Endocrinology (2023) Vol. 14
Open Access | Times Cited: 30
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
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
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
Pre-Pregnancy Obesity vs. Other Risk Factors in Probability Models of Preeclampsia and Gestational Hypertension
Małgorzata Lewandowska, Barbara Więckowska, Stefan Sajdak, et al.
Nutrients (2020) Vol. 12, Iss. 9, pp. 2681-2681
Open Access | Times Cited: 39
Artificial Intelligence in Pregnancy: A Scoping Review
Andreea M. Oprescu, G. Miró Amarante, Lutgardo García‐Díaz, et al.
IEEE Access (2020) Vol. 8, pp. 181450-181484
Open Access | Times Cited: 35
Novel electronic health records applied for prediction of pre-eclampsia: Machine-learning algorithms
Yixin Li, Xiaoping Shen, Chao Yang, et al.
Pregnancy Hypertension (2021) Vol. 26, pp. 102-109
Open Access | Times Cited: 28
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: 4
Can short‐ and long‐term maternal and infant risks linked to hypertension and diabetes during pregnancy be reduced by therapy?
Olof Stephansson, Anna Sandström
Journal of Internal Medicine (2024) Vol. 296, Iss. 3, pp. 216-233
Open Access | Times Cited: 4
Artificial Intelligence and Machine Learning in Preeclampsia
Anita T. Layton
Arteriosclerosis Thrombosis and Vascular Biology (2025)
Closed Access
Preeclampsia screening and prevention—A Nordic perspective
C. K. Ekelund, Ylva Carlsson, Lina Bergman, et al.
Acta Obstetricia Et Gynecologica Scandinavica (2025)
Open Access
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
Reviewing Accuracy of First Trimester Screening for Preeclampsia Using Maternal Factors and Biomarkers
Sarah Malone, Rani Haj Yahya, Stefan C. Kane
International Journal of Women s Health (2022) Vol. Volume 14, pp. 1371-1384
Open Access | Times Cited: 16
Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy
Saman Ghazvini, Saji Uthaman, Lilly Synan, et al.
Bioengineering & Translational Medicine (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 9
Determinants of Pregnancy-Induced Hypertension among Mothers Attending Public Hospitals in Wolaita Zone, South Ethiopia: Findings from Unmatched Case-Control Study
Yitagesu Belayhun, Yibeltal Kassa, Niguse Mekonnen, et al.
International Journal of Hypertension (2021) Vol. 2021, pp. 1-9
Open Access | Times Cited: 20
Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study
Dongying Zheng, Xinyu Hao, Muhanmmad Khan, et al.
Frontiers in Cardiovascular Medicine (2022) Vol. 9
Open Access | Times Cited: 13
Novel Associations Between Mid-Pregnancy Cardiovascular Biomarkers and Preeclampsia: An Explorative Nested Case-Control Study
Paliz Nordlöf Callbo, Katja Junus, Katja Gabrysch, et al.
Reproductive Sciences (2024) Vol. 31, Iss. 5, pp. 1391-1400
Open Access | Times Cited: 2
The Stockholm–Gotland perinatal cohort—A population‐based cohort including longitudinal data throughout pregnancy and the postpartum period
Kari Johansson, Michaela Granfors, Gunnar Petersson, et al.
Paediatric and Perinatal Epidemiology (2022) Vol. 37, Iss. 4, pp. 276-286
Open Access | Times Cited: 10
Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning
Seung Mi Lee, Yonghyun Nam, E. Choi, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 9
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.
(2024)
Open Access | Times Cited: 1
Identifying Predictor Variables for a Composite Risk Prediction Tool for Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Modified Delphi Study
Stephanie Cowan, Sarah Lang, Rebecca F. Goldstein, et al.
Healthcare (2024) Vol. 12, Iss. 13, pp. 1361-1361
Open Access | Times Cited: 1
Risk Assessment for Preeclampsia in the Preconception Period Based on Maternal Clinical History via Machine Learning Methods
Yeliz Kaya, Zafer Bütün, Özer Çelik, et al.
Journal of Clinical Medicine (2024) Vol. 14, Iss. 1, pp. 155-155
Open Access | Times Cited: 1
Routinely collected antenatal data for longitudinal prediction of preeclampsia in nulliparous women: a population-based study
Anna Sandström, Jonathan M. Snowden, Matteo Bottai, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 10
Page 1 - Next Page

Scroll to top