ReviewTrends in molecular medicine2021
Data-Driven Modeling of Pregnancy-Related Complications.
Review in Trends in molecular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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
37 citing papers in PubMed, 62 citations in OpenAlex.
- Unsupervised Machine Learning for the Identification of Latent First-Trimester Obstetric Phenotypes Associated with Maternal and Perinatal Morbidity.Diagnostics (Basel, Switzerland) · 2026Article
- Mendelian randomization study on the effects of women's reproductive traits and socioeconomic traits on pregnancy complications.BMC pregnancy and childbirth · 2026Article
- Innate Immunity and Microbial Recognition in Reproduction: From Barrier Defense to Maternal-Fetal Tolerance.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Review
- Multiomics characterization of acute child illness and mortality in Africa and South Asia.Nature communications · 2026Article
- Artificial Intelligence in Recurrent Pregnancy Loss: Current Evidence, Limitations, and Future Directions.Journal of clinical medicine · 2026Review
- Transforming perinatal health with AI-predictive models in precision and digital innovations for maternal and fetal well-being: a systematic review.Oxford open digital health · 2026Article
- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.Frontiers in digital health · 2026Review
- Mathematical Modeling of Obstetric Variables: Influence of COVID-19, Periodontal Disease and Dental Care During Pregnancy.Biomedicines · 2025Article
- Impact of maternal body mass index (BMI) and gestational weight gain on offspring's weight and BMI z-scores across the first 8 years of life.Clinical obesity · 2025Article
- Serum metabolites as diagnostic biomarkers for preterm labor: a metabolomics-based study.BMC pregnancy and childbirth · 2025Article
- Placental network differences among obstetric syndromes identified with an integrated multiomics approach.Communications biology · 2025Article
- Review
- Developmental toxicity: artificial intelligence-powered assessments.Trends in pharmacological sciences · 2025Review
- Advancing neonatal health: the promise and challenges of universal genome sequencing in newborn screening.Pediatric research · 2025Article
- Preeclampsia prediction and diagnosis: a comprehensive historical review from clinical insights to omics perspectives.Frontiers in medicine · 2025Review
- Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium.Molecular & cellular proteomics : MCP · 2024Article
- Predicting Spontaneous Preterm Birth Using the Immunome.Clinics in perinatology · 2024Review
- Computational Approaches for Predicting Preterm Birth and Newborn Outcomes.Clinics in perinatology · 2024Review
- Computational Approaches for Connecting Maternal Stress to Preterm Birth.Clinics in perinatology · 2024Review
- Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.Cell reports. Medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.
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.