ArticleNPJ digital medicine2022
Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- Inconsistencies in recommendations for the preventive management of pre-eclampsia and quality assessment of international guidelines: a systematic review.BMC pregnancy and childbirth · 2026Pooled it
- Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- A Machine Learning Framework for Preeclampsia Prediction at Isidro Ayora Hospital, Ecuador.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications.Current opinion in anaesthesiology · 2026Review
- Eclampsia risk prediction across diverse U.S. populations using CDC data: machine learning versus ACOG checklists.AJOG global reports · 2026Article
- Rethinking Risk Prediction in Preeclampsia: From Biomarkers to Mechanistic Phenotypes and Longitudinal Models.International journal of molecular sciences · 2026Review
- First-trimester biochemical inflammatory markers (NHR and NLR) as predictors of preeclampsia risk.Journal of medical biochemistry · 2026Article
- Machine Learning for Dynamic and Short-Term Prediction of Preeclampsia Using Routine Clinical Data.JAMA network open · 2026Article
- Development of a Triage Tool for Stratification and Referral of Women at Risk of Preeclampsia in Low-Resource Antenatal Settings: A Prospective Cohort Study.Health science reports · 2026Article
- Validation of a machine-learning-based algorithm to predict preeclampsia-related adverse outcomes on a real-world dataset.Archives of gynecology and obstetrics · 2026Article
- Preeclampsia and environmental epigenomics: the emerging role of air pollution, gut microbiome, and maternal exposures in disease programming.Environmental epigenetics · 2026Review
- Clinical Deployment of Interpretable AI: Bridging Routine Clinical Tests and Proteomic Signatures for Preeclampsia Risk Stratification.Current drug targets · 2026Article
- Genetic factors in the risk assessment of preeclampsia: a review of recent findings.Molecular biology reports · 2025Review
- Machine learning predictive system to predict the risk of developing pre-eclampsia.BMJ health & care informatics · 2025Article
- Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.Journal of medical Internet research · 2025Article
- Novel biomarkers for preeclampsia: Promises and pitfalls.Current opinion in obstetrics & gynecology · 2025Review
- Leveraging retinal vascular features in non-invasive, early diagnosis of preeclampsia.NPJ digital medicine · 2025Article
- Early prediction of preeclampsia from clinical, multi-omics and laboratory data using random forest model.BMC pregnancy and childbirth · 2025Article
- Prediction of high-risk pregnancy based on machine learning algorithms.Scientific reports · 2025Article
- Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model.BMC medical informatics and decision making · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments. We developed a digital phenotyping algorithm to curate 108,557 pregnancies from EMRs across the Mount Sinai Health System, accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset (N = 60,879) to construct predictive models of preeclampsia across three major pregnancy time periods (ante-, intra-, and postpartum). The resulting models predicted preeclampsia with high accuracy across the different pregnancy periods, with areas under the receiver operating characteristic curves (AUC) of 0.92, 0.82, and 0.89 at 37 gestational weeks, intrapartum and postpartum, respectively. We observed comparable performance in two independent patient cohorts. While our machine learning approach identified known risk factors of preeclampsia (such as blood pressure, weight, and maternal age), it also identified other potential risk factors, such as complete blood count related characteristics for the antepartum period. Our model not only has utility for earlier identification of patients at risk for preeclampsia, but given the prediction accuracy exceeds what is currently achieved in clinical practice, our model provides a path for promoting personalized precision therapeutic strategies for patients at risk.
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.