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Prediction model development of late-onset preeclampsia using machine learning-based methods
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/jama.2015.18421openalex-snapshot
Machine Learning and the Profession of Medicine
inferredcites →https://doi.org/10.1001/jamacardio.2016.3956openalex-snapshot
Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure
inferredcites →https://doi.org/10.1007/s00592-013-0459-9openalex-snapshot
Association between glucose variability as assessed by continuous glucose monitoring (CGM) and diabetic retinopathy in type 1 and type 2 diabetes
inferredcites →https://doi.org/10.1016/j.ajog.2009.06.033openalex-snapshot
Long-term maternal and subsequent pregnancy outcomes 5 years after hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome
inferredcites →https://doi.org/10.1016/j.ajog.2013.09.010openalex-snapshot
Placental pathology suggesting that preeclampsia is more than one disease
inferredcites →https://doi.org/10.1016/j.ajog.2016.03.001openalex-snapshot
Placental origins of adverse pregnancy outcomes: potential molecular targets: an Executive Workshop Summary of the Eunice Kennedy Shriver National Institute of Child Health and Human Development
inferredcites →https://doi.org/10.1016/j.ajog.2017.03.017openalex-snapshot
Preeclampsia: novel insights from global RNA profiling of trophoblast subpopulations
inferredcites →https://doi.org/10.1016/j.ajog.2017.10.024openalex-snapshot
First-trimester screening for early and late preeclampsia using maternal characteristics, biomarkers, and estimated placental volume
inferredcites →https://doi.org/10.1016/j.bpobgyn.2011.01.002openalex-snapshot
Early prediction and prevention of pre-eclampsia
inferredcites →https://doi.org/10.1016/j.clnu.2014.12.001openalex-snapshot
Association between magnesium status, oxidative stress and inflammation in preeclampsia: A case–control study
inferredcites →https://doi.org/10.1016/j.ijmedinf.2017.09.006openalex-snapshot
Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics
inferredcites →https://doi.org/10.1016/j.semnephrol.2010.10.002openalex-snapshot
The Kidney in Normal Pregnancy and Preeclampsia
inferredcites →https://doi.org/10.1016/s0095-5108(18)30491-3openalex-snapshot
The Renin-Angiotensin-Aldosterone System in Preeclampsia
inferredcites →https://doi.org/10.1016/s0140-6736(10)61351-7openalex-snapshot
Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model
inferredcites →https://doi.org/10.1016/s0140-6736(96)11196-xopenalex-snapshot
Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions
inferredcites →https://doi.org/10.1016/s0167-9473(01)00065-2openalex-snapshot
Stochastic gradient boosting
inferredcites →https://doi.org/10.1053/j.ackd.2013.02.003openalex-snapshot
New Developments in the Pathogenesis of Preeclampsia
inferredcites →https://doi.org/10.1056/nejmoa1414838openalex-snapshot
Predictive Value of the sFlt-1:PlGF Ratio in Women with Suspected Preeclampsia
inferredcites →https://doi.org/10.1056/nejmoa1606148openalex-snapshot
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inferredcites →https://doi.org/10.1056/nejmp1606181openalex-snapshot
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inferredcites →https://doi.org/10.1093/bib/bbt020openalex-snapshot
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inferredcites →https://doi.org/10.1093/ndt/gfs278openalex-snapshot
From preeclampsia to renal disease: a role of angiogenic factors and the renin-angiotensin aldosterone system?
inferredcites →https://doi.org/10.1097/01.aog.0000437382.03963.88openalex-snapshot
Hypertension in Pregnancy
inferredcites →https://doi.org/10.1097/mnh.0000000000000105openalex-snapshot
Pathogenesis of preeclampsia
inferredcites →https://doi.org/10.1111/j.2517-6161.1958.tb00292.xopenalex-snapshot
The Regression Analysis of Binary Sequences
inferredcites →https://doi.org/10.1117/1.2819119openalex-snapshot
Book Review: Pattern Recognition and Machine Learning
inferredcites →https://doi.org/10.1136/bmj.d1875openalex-snapshot
Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort
inferredcites →https://doi.org/10.1136/bmj.f6564openalex-snapshot
Pre-eclampsia rates in the United States, 1980-2010: age-period-cohort analysis
inferredcites →https://doi.org/10.1136/bmj.g2301openalex-snapshot
Chronic hypertension and pregnancy outcomes: systematic review and meta-analysis
inferredcites →https://doi.org/10.1159/000351178openalex-snapshot
Association between Dietary Sodium and Potassium Intake with Chronic Kidney Disease in US Adults: A Cross-Sectional Study
inferredcites →https://doi.org/10.1159/000381778openalex-snapshot
Evaluation of High-Sensitivity C-Reactive Protein and Serum Lipid Profile in Southeastern Nigerian Women with Pre-Eclampsia
inferredcites →https://doi.org/10.1161/circulationaha.113.003215openalex-snapshot
Diagnostic Accuracy of Placental Growth Factor in Women With Suspected Preeclampsia
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Beat-to-Beat, Reading-to-Reading, and Day-to-Day Blood Pressure Variability in Relation to Organ Damage in Untreated Chinese
inferredcites →https://doi.org/10.1260/2040-2295.4.4.465openalex-snapshot
Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review
inferredcites →https://doi.org/10.1371/journal.pmed.1001589openalex-snapshot
A Risk Prediction Model for the Assessment and Triage of Women with Hypertensive Disorders of Pregnancy in Low-Resourced Settings: The miniPIERS (Pre-eclampsia Integrated Estimate of RiSk) Multi-country Prospective Cohort Study
inferredcites →https://doi.org/10.1371/journal.pone.0206410openalex-snapshot
Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition
inferredcites →https://doi.org/10.2471/blt.13.127464openalex-snapshot
A prospective study of maternal, fetal and neonatal deaths in low- and middle-income countries
inferredcites →https://doi.org/10.3109/10641955.2013.877925openalex-snapshot
Calcium level during the first trimester of pregnancy as a predictor of preeclampsia
inferredcites →https://doi.org/10.3109/10641969709083181openalex-snapshot
The Renin — Angiotensin — Aldosterone System in Pre-Eclampsia
inferredcites →https://doi.org/10.3109/14767050903544736openalex-snapshot
Low potassium level during the first half of pregnancy is associated with lower risk for the development of gestational diabetes mellitus and severe pre-eclampsia
inferredcites →https://doi.org/10.3310/hta21180openalex-snapshot
Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP): a prospective cohort study
inferredcites →https://doi.org/10.3310/hta21410openalex-snapshot
Effects of antenatal diet and physical activity on maternal and fetal outcomes: individual patient data meta-analysis and health economic evaluation
inferredcites →https://doi.org/10.4048/jbc.2012.15.2.230openalex-snapshot
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Pattern recognition and machine learning
inferredcites →https://doi.org/10.5860/choice.44-5091openalex-snapshot
Pattern recognition and machine learning
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.3530598openalex-extracted
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inferred← citeshttps://doi.org/10.1001/jamanetworkopen.2022.43146openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9781119865728.ch14openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/nur.22122openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/uog.27510openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/uog.27668openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-030-87735-4_14openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-07654-1_3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-38430-1_21openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-85398-2_28openalex-snapshot
Early Detection of Preeclampsia: A Predictive Approach for Maternal Health
inferred← citeshttps://doi.org/10.1007/978-3-032-19303-2_14openalex-snapshot
Integration of Machine Learning Models for High-Risk Pregnancy Screening in Rural Primary Healthcare
inferred← citeshttps://doi.org/10.1007/978-981-97-5581-3_17openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-981-97-9327-3_36openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-981-99-2322-9_50openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00129-022-04994-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00404-022-06864-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00404-025-08261-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11255-025-04807-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11906-024-01297-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41666-023-00128-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41666-023-00151-4openalex-snapshot
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
inferred← citeshttps://doi.org/10.1007/s43032-022-00951-wopenalex-snapshot
Vision for Improving Pregnancy Health: Innovation and the Future of Pregnancy Research
inferred← citeshttps://doi.org/10.1007/s44163-026-01280-6openalex-snapshot
A scoping review on the use of Artificial Intelligence applications in women’s health (MARIE WP1)
inferred← citeshttps://doi.org/10.1016/b978-0-12-820675-1.00010-1openalex-snapshot
Gas chromatographic analysis of lipids
inferred← citeshttps://doi.org/10.1016/b978-0-443-22008-1.00010-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-443-26745-1.00006-2openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ajog.2022.01.026openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ajog.2024.02.299openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ajogmf.2020.100100openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ajogmf.2022.100792openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.asoc.2026.116072openalex-snapshot
Adaptive CNN ensemble weighting via ABC optimization for maternal health risk prediction
inferred← citeshttps://doi.org/10.1016/j.bpobgyn.2025.102627openalex-snapshot
Routine mid-gestational prediction of later preeclampsia
inferred← citeshttps://doi.org/10.1016/j.ebiom.2020.102710openalex-snapshot
Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia
inferred← citeshttps://doi.org/10.1016/j.ejogrb.2022.05.033openalex-snapshot
Preeclampsia subtypes: Clinical aspects regarding pathogenesis, signs, and management with special attention to diuretic administration
inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2024.105645openalex-snapshot
Prediction of pre-eclampsia with machine learning approaches: Leveraging important information from routinely collected data
inferred← citeshttps://doi.org/10.1016/j.ins.2024.120556openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.intimp.2024.112238openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jogoh.2020.102048openalex-snapshot
Artificial intelligence: The current state of affairs for AI in pregnancy and labour
inferred← citeshttps://doi.org/10.1016/j.jri.2023.104190openalex-snapshot
The role of first trimester serum inflammatory indexes (NLR, PLR, MLR, SII, SIRI, and PIV) and the β-hCG to PAPP-A ratio in predicting preeclampsia
inferred← citeshttps://doi.org/10.1016/j.medntd.2022.100158openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.placenta.2025.03.003openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.preghy.2021.10.006openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.preghy.2025.101269openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.xagr.2024.100386openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41467-025-58437-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-022-15391-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-022-16273-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-024-71934-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s44294-024-00015-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1051/e3sconf/202344802057openalex-snapshot
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inferred← citeshttps://doi.org/10.1051/e3sconf/202344802058openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/01443615.2023.2232656openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/07853890.2024.2357742openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/10408363.2020.1857681openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/10641955.2023.2225617openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/10641955.2024.2312165openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/14779072.2023.2223978openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/20476965.2024.2435845openalex-snapshot
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inferred← citeshttps://doi.org/10.1089/jwh.2020.8889openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/ajh/hpaf052openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/jalm/jfaa124openalex-snapshot
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inferred← citeshttps://doi.org/10.1097/aog.0000000000005944openalex-snapshot
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inferred← citeshttps://doi.org/10.1097/aog.0000000000006040openalex-snapshot
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inferred← citeshttps://doi.org/10.1097/md.0000000000034878openalex-snapshot
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inferred← citeshttps://doi.org/10.1097/md.0000000000045555openalex-snapshot
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inferred← citeshttps://doi.org/10.1101/2022.04.03.22273366openalex-snapshot
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inferred← citeshttps://doi.org/10.1101/2022.06.08.22276107openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/bibm49941.2020.9313108openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/embc40787.2023.10340404openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/esai67033.2025.11438364openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/gcat59970.2023.10353259openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/hsi61632.2024.10613547openalex-snapshot
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inferred← citeshttps://doi.org/10.1111/ggi.14097openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s12884-023-06220-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s12884-024-06821-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s12884-024-06988-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.12688/wellcomeopenres.25574.1openalex-snapshot
Predictive models for stillbirths and neonatal deaths: Protocol for a global scoping review with focus on sub-Saharan Africa.
inferred← citeshttps://doi.org/10.1371/journal.pmed.1004509openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pone.0231166openalex-snapshot
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Modern possibilities of prediction and early diagnosis of preeclampsia
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Artificial intelligence in reproductive medicine
inferred← citeshttps://doi.org/10.17749/2313-7347/ob.gyn.rep.2021.229openalex-snapshot
Artificial intelligence technologies in predicting preeclampsia
inferred← citeshttps://doi.org/10.17749/2313-7347/ob.gyn.rep.2026.721openalex-snapshot
Application of artificial intelligence models trained on multiparametric datasets for predicting pregnancy complications
inferred← citeshttps://doi.org/10.17749/2313-7347/ob.gyn.rep.2026.729openalex-snapshot
Comparative effectiveness of eight machine learning algorithms and the FMF competing risks model for first-trimester preeclampsia prediction: a multicenter cohort study with external validation
inferred← citeshttps://doi.org/10.18280/isi.280225openalex-snapshot
A Hybrid Resampling Method with K-Nearest Neighbour (FHR-KNN) for Imbalanced Preeclampsia Dataset
inferred← citeshttps://doi.org/10.18332/ejm/143166openalex-snapshot
Intelligent systems in obstetrics and midwifery: Applications of machine learning
inferred← citeshttps://doi.org/10.18502/ijph.v53i11.16941openalex-snapshot
Preeclampsia: Etiology, Pathophysiology, Risk Factors, Impact and Prevention: A Narrative Review
inferred← citeshttps://doi.org/10.20473/mog.v32i32024.214-222openalex-snapshot
Current preeclampsia prediction model and biomarker
inferred← citeshttps://doi.org/10.21037/atm-22-4192openalex-snapshot
Development and validation of the prediction models for preeclampsia: a retrospective, single-center, case-control study
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10350493/v1openalex-snapshot
Machine learning-based prediction of preeclampsia: a comparative study on model effectiveness
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2635419/v1openalex-snapshot
A Comprehensive and Bias-Free Machine Learning Approach for Risk Prediction of Preeclampsia with Severe Features in a Nulliparous Study Cohort
inferred← citeshttps://doi.org/10.21203/rs.3.rs-36772/v1openalex-snapshot
Association Between Alcohol use in Pregnancy and Preeclampsia or Hypertension in Pregnancy: A Systematic Review
inferred← citeshttps://doi.org/10.21203/rs.3.rs-44764/v1openalex-snapshot
Predicting Risk of Chronic Hypertension in Women with Preeclampsia Based on Placenta Histology. A Prospective Cohort Study in Cuba.
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4957726/v1openalex-snapshot
IGFBP1 and routine laboratory Indicators for early prediction of pre-eclampsia in Chinese population
inferred← citeshttps://doi.org/10.21203/rs.3.rs-6966953/v1openalex-snapshot
Supervised classification of Preeclampsia clinical cases using datasets from MALDI-TOF-MS and machine learning tools
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9292292/v1openalex-snapshot
Evaluating the predictive power of machine learning in preeclampsia
inferred← citeshttps://doi.org/10.2147/ijgm.s392915openalex-snapshot
Detecting Preeclampsia Severity Using Maternal-Obstetrical Characteristics and Complete Blood Cell Counts
inferred← citeshttps://doi.org/10.2147/rmhp.s297838openalex-snapshot
The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms
inferred← citeshttps://doi.org/10.2174/0929867328666211108110731openalex-snapshot
Top 100 Most-Cited Publications on Breast Cancer and Machine Learning Research: A Bibliometric Analysis
inferred← citeshttps://doi.org/10.2174/1573404820666230227120828openalex-snapshot
Predicting Preeclampsia Using Principal Component Analysis and Decision Tree Classifier
inferred← citeshttps://doi.org/10.2196/16503openalex-snapshot
Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis
inferred← citeshttps://doi.org/10.2196/33835openalex-snapshot
The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study
inferred← citeshttps://doi.org/10.2196/48487openalex-snapshot
Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study
inferred← citeshttps://doi.org/10.2196/88450openalex-snapshot
Machine Learning–Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: Multicenter Model Development Study
inferred← citeshttps://doi.org/10.25248/reas.e16231.2024openalex-snapshot
Métodos de inteligência artificial na predição e diagnóstico precoces de complicações na gravidez
inferred← citeshttps://doi.org/10.29393/tdudec-28md1de28openalex-snapshot
Desarrollo y evaluación de métodos multivariados basados en datos gineco-obstétricos y espectrales de suero para la predicción de diabetes gestacional en el primer trimestre del embarazo.
inferred← citeshttps://doi.org/10.31083/ceog48538openalex-snapshot
Machine Learning-Based Early Prediction of Selective Fetal Growth Restriction in Monochorionic Diamniotic Twins Using Crown–Rump Length Discordance and Abnormal Placental Cord Insertion
inferred← citeshttps://doi.org/10.31083/j.ceog5110231openalex-snapshot
Value of Maternal Renal Function Indicators in Predicting Small for Gestational Age Infants in Patient with Severe Preeclampsia
inferred← citeshttps://doi.org/10.3233/shti210927openalex-snapshot
Contribution of Artificial Intelligence in Pregnancy: A Scoping Review
inferred← citeshttps://doi.org/10.32604/cmc.2023.035710openalex-snapshot
An Automated System for Early Prediction of Miscarriage in the First Trimester Using Machine Learning
inferred← citeshttps://doi.org/10.32604/csse.2023.036598openalex-snapshot
Data Analytics on Unpredictable Pregnancy Data Records Using Ensemble Neuro-Fuzzy Techniques
inferred← citeshttps://doi.org/10.33421/inmp.2019168openalex-snapshot
Modelo predictivo de preeclampsia según el consumo de macronutrientes mediante aprendizaje automático en un hospital de Lima, 2019
inferred← citeshttps://doi.org/10.3389/978-2-8325-4863-9openalex-snapshot
Adverse outcomes of preeclampsia: From mother to baby, pregnancy to postpartum
inferred← citeshttps://doi.org/10.3389/fbioe.2021.780389openalex-snapshot
Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review
inferred← citeshttps://doi.org/10.3389/fcvm.2022.910701openalex-snapshot
Prediction of Delivery Within 7 Days After Diagnosis of Early Onset Preeclampsia Using Machine-Learning Models
inferred← citeshttps://doi.org/10.3389/fcvm.2022.932480openalex-snapshot
Novel Early Pregnancy Multimarker Screening Test for Preeclampsia Risk Prediction
inferred← citeshttps://doi.org/10.3389/fcvm.2022.959649openalex-snapshot
Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study
inferred← citeshttps://doi.org/10.3389/fcvm.2023.1148376openalex-snapshot
Uric acid/superoxide dismutase can predict progression of gestational hypertension to preeclampsia
inferred← citeshttps://doi.org/10.3389/fcvm.2024.1360238openalex-snapshot
AI-based preeclampsia detection and prediction with electrocardiogram data
inferred← citeshttps://doi.org/10.3389/fdgth.2022.831123openalex-snapshot
The Desire of Medical Students to Integrate Artificial Intelligence Into Medical Education: An Opinion Article
inferred← citeshttps://doi.org/10.3389/fendo.2023.1081667openalex-snapshot
Multi-omics and machine learning for the prevention and management of female reproductive health
inferred← citeshttps://doi.org/10.3389/fendo.2023.1130139openalex-snapshot
Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications
inferred← citeshttps://doi.org/10.3389/fgwh.2026.1672534openalex-snapshot
The clinical value of nomogram combined with machine learning models in predicting the progression of hypertensive disorders in pregnancy to severe preeclampsia
inferred← citeshttps://doi.org/10.3389/fmed.2023.1144170openalex-snapshot
Preeclampsia pathophysiology and adverse outcomes during pregnancy and postpartum
inferred← citeshttps://doi.org/10.3389/fmed.2025.1689745openalex-snapshot
Preeclampsia prediction and diagnosis: a comprehensive historical review from clinical insights to omics perspectives
inferred← citeshttps://doi.org/10.3389/fphys.2022.850149openalex-snapshot
A New Model for the Predicting the Risk of Preeclampsia in Twin Pregnancy
inferred← citeshttps://doi.org/10.3389/fphys.2022.896969openalex-snapshot
Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China
inferred← citeshttps://doi.org/10.3389/fphys.2023.1143543openalex-snapshot
Potential biomarkers for late-onset and term preeclampsia: A scoping review
inferred← citeshttps://doi.org/10.3390/bdcc7010032openalex-snapshot
Prediction of Preeclampsia Using Machine Learning and Deep Learning Models: A Review
inferred← citeshttps://doi.org/10.3390/biomedicines13020347openalex-snapshot
Prediction Models for Late-Onset Preeclampsia: A Study Based on Logistic Regression, Support Vector Machine, and Extreme Gradient Boosting Models
inferred← citeshttps://doi.org/10.3390/biomedicines13061483openalex-snapshot
An Explainable Fuzzy Framework for Assessing Preeclampsia Classification
inferred← citeshttps://doi.org/10.3390/biomedicines13081958openalex-snapshot
Early-Onset Versus Late-Onset Preeclampsia in Bogotá, Colombia: Differential Risk Factor Identification and Evaluation Using Traditional Statistics and Machine Learning
inferred← citeshttps://doi.org/10.3390/clinpract14040109openalex-snapshot
Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension
inferred← citeshttps://doi.org/10.3390/diagnostics13020287openalex-snapshot
Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study
inferred← citeshttps://doi.org/10.3390/diagnostics16010132openalex-snapshot
Diagnostic Gap in Rural Maternal Health: Initial Validation of a Parsimonious Clinical Model for Hypertensive Disorders of Pregnancy in a Honduran Hospital
inferred← citeshttps://doi.org/10.3390/healthcare10112164openalex-snapshot
Application of Artificial Intelligence in Screening for Adverse Perinatal Outcomes—A Systematic Review
inferred← citeshttps://doi.org/10.3390/healthcare12131361openalex-snapshot
Identifying Predictor Variables for a Composite Risk Prediction Tool for Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Modified Delphi Study
inferred← citeshttps://doi.org/10.3390/healthcare13070833openalex-snapshot
Maternal Health Risk Detection: Advancing Midwifery with Artificial Intelligence
inferred← citeshttps://doi.org/10.3390/healthcare13101162openalex-snapshot
Improved Prediction Accuracy for Late-Onset Preeclampsia Using cfRNA Profiles: A Comparative Study of Marker Selection Strategies
inferred← citeshttps://doi.org/10.3390/ijerph18062954openalex-snapshot
Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods
inferred← citeshttps://doi.org/10.3390/ijerph20075335openalex-snapshot
Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach
inferred← citeshttps://doi.org/10.3390/ijms242115716openalex-snapshot
Top–Down Proteomics of Human Saliva, Analyzed with Logistic Regression and Machine Learning Methods, Reveal Molecular Signatures of Ovarian Cancer
inferred← citeshttps://doi.org/10.3390/informatics12040141openalex-snapshot
Combining Fuzzy Cognitive Maps and Metaheuristic Algorithms to Predict Preeclampsia and Intrauterine Growth Restriction
inferred← citeshttps://doi.org/10.3390/jcdd9020055openalex-snapshot
Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders
inferred← citeshttps://doi.org/10.3390/jcm12020418openalex-snapshot
Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study
inferred← citeshttps://doi.org/10.3390/jcm14072498openalex-snapshot
Practical Considerations Concerning Preeclampsia Subgroups
inferred← citeshttps://doi.org/10.3390/jcm14092970openalex-snapshot
Prevention of Pre-Eclampsia: Modern Strategies and the Role of Early Screening
inferred← citeshttps://doi.org/10.3390/jcm14196974openalex-snapshot
Smart Pregnancy: AI-Driven Approaches to Personalised Maternal and Foetal Health—A Scoping Review
inferred← citeshttps://doi.org/10.3390/life11101052openalex-snapshot
C1431T Variant of PPARγ Is Associated with Preeclampsia in Pregnant Women
inferred← citeshttps://doi.org/10.3390/life13081648openalex-snapshot
Risk Prediction Model of Early-Onset Preeclampsia Based on Risk Factors and Routine Laboratory Indicators
inferred← citeshttps://doi.org/10.3390/make6040123openalex-snapshot
A Review on Machine Learning Deployment Patterns and Key Features in the Prediction of Preeclampsia
inferred← citeshttps://doi.org/10.3390/medicina60121909openalex-snapshot
Continuous Risk Assessment of Late and Term Preeclampsia Throughout Pregnancy: A Retrospective Cohort Study
inferred← citeshttps://doi.org/10.4018/978-1-6684-8974-1.ch010openalex-snapshot
Prediction of Preeclampsia in Pregnant Women Using Machine Learning Paradigm
inferred← citeshttps://doi.org/10.4103/njcp.njcp_222_23openalex-snapshot
Artificial Intelligence in Early Diagnosis of Preeclampsia
inferred← citeshttps://doi.org/10.47879/ed.ep.2024479p97openalex-snapshot
MÉTODOS CONTEMPORÂNEOS DE PREVENÇÃO E MANEJO DA HIPERTENSÃO NA GESTAÇÃO
inferred← citeshttps://doi.org/10.51983/ajsat-2023.12.2.4072openalex-snapshot
Enhancing Maternal Outcome Prediction Using Explainable Artificial Intelligence for Women of Childbearing Age
inferred← citeshttps://doi.org/10.5812/semj-153673openalex-snapshot
Information Requirements of Clinical Decision Support Systems for the Diagnosis and Prediction of Preeclampsia
inferred← citeshttps://doi.org/10.6084/m9.figshare.21019177openalex-snapshot
Additional file 1 of Screening high-risk population of persistent postpartum hypertension in women with preeclampsia using latent class cluster analysis
inferred← citeshttps://doi.org/10.7759/cureus.115127openalex-snapshot
Evaluating Maternal and Fetal Risk in Preeclampsia Using Thyroid and Inflammatory Markers: A Naive Bayes and Network Analysis Approach
inferred← citeshttps://doi.org/10.7759/cureus.76095openalex-snapshot
Prediction of Preeclampsia Using Machine Learning: A Systematic Review
inferred← citeshttps://doi.org/10.7759/cureus.83961openalex-snapshot
Artificial Intelligence Applications in Obstetric Risk Prediction: A Systematic Review of Machine Learning Models for Preeclampsia
inferred← citeshttps://doi.org/10.7759/s44389-026-00298-2openalex-snapshot
Hybrid Boosting Ensemble for Pre-Eclampsia Prediction Using Clinical Data
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