ArticleJournal of clinical medicine2023
Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia-A Prospective Study.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Machine Learning Prediction Models for Preeclampsia: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review.Current hypertension reports · 2024Pooled it
- Unsupervised Machine Learning for the Identification of Latent First-Trimester Obstetric Phenotypes Associated with Maternal and Perinatal Morbidity.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence analysis of minimally invasive surgery data.Journal of robotic surgery · 2026Review
- Artificial Intelligence and Machine Learning for predicting Major Obstetric Emergencies: Current Evidence, Clinical Translation, and Future Directions.International journal of women's health · 2026Review
- Identifying mitochondrial genes and potential biological functions in pre-eclampsia: bioinformatics and experimental insights.Frontiers in genetics · 2026Article
- Integrating urine metabolomic biomarkers and machine learning algorithms to predict preeclampsia.European journal of medical research · 2025Article
- Early-Onset Versus Late-Onset Preeclampsia in Bogotá, Colombia: Differential Risk Factor Identification and Evaluation Using Traditional Statistics and Machine Learning.Biomedicines · 2025Article
- Review
- Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features.NPJ digital medicine · 2025Article
- Presenting a prediction model for HELLP syndrome through data mining.BMC medical informatics and decision making · 2025Article
- Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.BMC medical informatics and decision making · 2025Article
- Preeclampsia prediction via machine learning: a systematic literature review.Health systems (Basingstoke, England) · 2025Review
- Factors Associated with Maternal Morbidity in Patients with Eclampsia in Three Obstetric Intensive Care Units: A Retrospective Study.Journal of clinical medicine · 2024Article
- Predicting Intra- and Postpartum Hemorrhage through Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2024Article
- Machine learning-based prediction of gastroparesis risk following complete mesocolic excision.Discover oncology · 2024Article
- Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms.Medicina (Kaunas, Lithuania) · 2024Article
- Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data.Diagnostics (Basel, Switzerland) · 2024Article
- Prediction of Intrauterine Growth Restriction and Preeclampsia Using Machine Learning-Based Algorithms: A Prospective Study.Diagnostics (Basel, Switzerland) · 2024Article
- Predicting the Feasibility of Curative Resection in Low Rectal Cancer: Insights from a Prospective Observational Study on Preoperative Magnetic Resonance Imaging Accuracy.Medicina (Kaunas, Lithuania) · 2024Observational
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between November 2019 and September 2022. The patients' clinical and paraclinical characteristics were evaluated in the first trimester and were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and their predictive performance was assessed. (3) Results: Early-onset PE was best predicted by DT (accuracy: 94.1%) and SVM (accuracy: 91.2%) models, while NB (accuracy: 98.6%) and RF (accuracy: 92.8%) models had the highest performance when used to predict all types of PE. The predictive performance of these models was modest for moderate and severe types of PE, with accuracies ranging from 70.6% and 82.4%. (4) Conclusions: The machine learning-based models could be useful tools for EO-PE prediction and could differentiate patients who will develop PE as early as the first trimester of pregnancy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.