ReviewArchives of gynecology and obstetrics2023
New advances in prediction and surveillance of preeclampsia: role of machine learning approaches and remote monitoring.
Review in Archives of gynecology and obstetrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis 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.
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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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cadmium Associated Preeclampsia: A Systematic Literature Review of Pregnancy and Birth Outcomes.Biological trace element research · 2025Pooled it
- The Immunopathology of Preeclampsia.Biomedicines · 2026Review
- Protocol for the external validation of machine learning models for risk prediction in early-onset preeclampsia: a multicenter prospective cohort study.BMC pregnancy and childbirth · 2026Article
- Extracting Signs and Symptoms of Hypertensive Disorders in Pregnancy from Clinical Notes Using Natural Language Processing.Maternal and child health journal · 2026Observational
- Unlocking the Full Potential of Health Care Teams: How Artificial Intelligence Can Help.JMIR AI · 2026Article
- The Landscape of Disulfidptosis in Preeclampsia Reveals a Novel 5-Gene Diagnostic Signature via Machine Learning.International journal of women's health · 2026Article
- Prevalence and Associated Factors of Frailty in Middle-Aged and Elderly Patients with Atrial Fibrillation: An Exploratory Machine Learning Analysis.Clinical interventions in aging · 2026Article
- The Role of Angiogenetic Factors in Preeclampsia.International journal of molecular sciences · 2025Review
- Evaluation of oxidative stress markers and the angiogenic factors in preeclampsia and associated features.BMC pregnancy and childbirth · 2025Article
- Balancing hormonal symphony: the dynamics of reproduction and pregnancy.BMC endocrine disorders · 2025Article
- Leveraging Machine Learning to Predict and Assess Disparities in Severe Maternal Morbidity in Maryland.Healthcare (Basel, Switzerland) · 2025Article
- Combined assessment of placental growth factor, uterine artery pulsation index, and mean arterial pressure for predicting preeclampsia.American journal of translational research · 2025Article
- Preeclampsia prediction via machine learning: a systematic literature review.Health systems (Basingstoke, England) · 2025Review
- Preeclampsia prediction and diagnosis: a comprehensive historical review from clinical insights to omics perspectives.Frontiers in medicine · 2025Review
- Angiogenic factors for early prediction of preeclampsia.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Article
- Biomarkers for Early Prediction and Management of Preeclampsia: A Comprehensive Review.Medical science monitor : international medical journal of experimental and clinical research · 2024Review
- Novel Associations Between Mid-Pregnancy Cardiovascular Biomarkers and Preeclampsia: An Explorative Nested Case-Control Study.Reproductive sciences (Thousand Oaks, Calif.) · 2024Article
- AI-based preeclampsia detection and prediction with electrocardiogram data.Frontiers in cardiovascular medicine · 2024Article
- Exploring the use of ChatGPT in OBGYN: a bibliometric analysis of the first ChatGPT-related publications.Archives of gynecology and obstetrics · 2023Article
- Reimagining Placental Perfusion in Preeclampsia: Integrating Doppler Ultrasound, Three-dimensional Vascular Indices, and Predictive Artificial Intelligence.Journal of medical ultrasoundReview
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preeclampsia, a multisystem disorder in pregnancy, is still one of the main causes of maternal morbidity and mortality. Due to a lack of a causative therapy, an accurate prediction of women at risk for the disease and its associated adverse outcomes is of utmost importance to tailor care. In the past two decades, there have been successful improvements in screening as well as in the prediction of the disease in high-risk women. This is due to, among other things, the introduction of biomarkers such as the sFlt-1/PlGF ratio. Recently, the traditional definition of preeclampsia has been expanded based on new insights into the pathophysiology and conclusive evidence on the ability of angiogenic biomarkers to improve detection of preeclampsia-associated maternal and fetal adverse events.However, with the widespread availability of digital solutions, such as decision support algorithms and remote monitoring devices, a chance for a further improvement of care arises. Two lines of research and application are promising: First, on the patient side, home monitoring has the potential to transform the traditional care pathway. The importance of the ability to input and access data remotely is a key learning from the COVID-19 pandemic. Second, on the physician side, machine-learning-based decision support algorithms have been shown to improve precision in clinical decision-making. The integration of signals from patient-side remote monitoring devices into predictive algorithms that power physician-side decision support tools offers a chance to further improve care.The purpose of this review is to summarize the recent advances in prediction, diagnosis and monitoring of preeclampsia and its associated adverse outcomes. We will review the potential impact of the ability to access to clinical data via remote monitoring. In the combination of advanced, machine learning-based risk calculation and remote monitoring lies an unused potential that allows for a truly patient-centered care.
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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 OpenQuestion graph.