SynthesisBMC pregnancy and childbirth2024
Machine learning models for predicting preeclampsia: a systematic review.
Synthesis in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
- Preeclampsia Genomic Susceptibility Factors in Populations of African Ancestry: A Systematic Review and Meta-Analysis.International journal of molecular sciences · 2026Pooled it
- Self-Reported Knowledge, Attitudes, Perceptions, and Readiness Regarding AI Among Obstetrics and Gynecology Trainees: Cross-Sectional Study.JMIR formative research · 2026Article
- Artificial Intelligence in Obstetrics: Current Trends and Future Directions.Journal of clinical medicine · 2026Review
- Article
- A Machine Learning Framework for Preeclampsia Prediction at Isidro Ayora Hospital, Ecuador.Diagnostics (Basel, Switzerland) · 2026Article
- Development of antenatal risk prediction model for preeclampsia with severe features in Lagos, Nigeria (PreSev study): protocol of a prospective cohort study.BMC pregnancy and childbirth · 2026Article
- Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review.Epidemiologia (Basel, Switzerland) · 2026Review
- Machine learning models for predicting postpartum convulsions using clinical indicators from PMA Ethiopia data.Scientific reports · 2026Article
- Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications.Current opinion in anaesthesiology · 2026Review
- Interpretable prediction of neonatal mortality and its key predictors using machine learning and SHAP analysis.BMC medical informatics and decision making · 2026Article
- Comparison of Machine Learning Models and the FMF Competing-Risks Algorithm for First-Trimester Preeclampsia Screening in a Romanian Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Rethinking Risk Prediction in Preeclampsia: From Biomarkers to Mechanistic Phenotypes and Longitudinal Models.International journal of molecular sciences · 2026Review
- The economic imperative of artificial intelligence in maternal and neonatal health: a review of evaluation benefits, frameworks, challenges, future perspectives, and limitations.Cost effectiveness and resource allocation : C/E · 2026Review
- Artificial Intelligence in Obstetrics and Gynecology Nursing: Clinical, Educational, and Ethical Perspectives.Cureus · 2026Review
- Modeling Factors Associated With Diarrhea Caused by Cryptosporidium Species Using Machine Learning Methods.Journal of clinical laboratory analysis · 2026Article
- Multi-omics insights into immune tolerance at the maternal-fetal interface in recurrent pregnancy loss: mechanisms, integration challenges, and translational perspectives.Frontiers in immunology · 2026Review
- From virtual pregnancy to digital twin obstetrics: multimodal data integration for personalized prediction of pregnancy complications.Frontiers in medicine · 2026Review
- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.Frontiers in digital health · 2026Review
- Artificial intelligence for early prediction of gestational diabetes mellitus and preeclampsia: a systematic review of machine learning models and clinical decision support systems.Frontiers in artificial intelligence · 2026Review
- A Machine Learning Approach to Predicting Household Smoke Exposure Risk in Somalia: An Analysis With SHAP Explanations.Environmental health insights · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis systematic review provides an overview of machine learning (ML) approaches for predicting preeclampsia.
methodThis review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. We searched the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus, and Google Scholar up to February 2023. Search terms were limited to "preeclampsia" AND "artificial intelligence" OR "machine learning" OR "deep learning." All studies that used ML-based analysis for predicting preeclampsia in pregnant women were considered. Non-English articles and those that are unrelated to the topic were excluded. The PROBAST was used to assess the risk of bias and applicability of each included study.
resultsThe search strategy yielded 128 citations; after duplicates were removed and title and abstract screening was completed, 18 full-text articles were evaluated for eligibility. Four studies were included in this review. Two studies were at low risk of bias, and two had low to moderate risk. All of the study designs included were retrospective cohort studies. Nine distinct models were chosen as ML models from the four studies. Maternal characteristics, medical history, medication intake, obstetrical history, and laboratory and ultrasound findings obtained during prenatal care visits were candidate predictors to train the ML model. Elastic net, stochastic gradient boosting, extreme gradient boosting, and Random forest were among the best models to predict preeclampsia. All four studies used metrics such as the area under the curve, true positive rate, negative positive rate, accuracy, precision, recall, and F1 score. The AUC of ML models varied from 0.860 to 0.973 in four studies.
conclusionThe results of studies yielded high prediction performance of ML models for preeclampsia risk from routine early pregnancy information.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.