ArticleBMC medicine2022
Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth.
Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Machine learning to predict adverse perinatal outcomes: a systematic review and meta-analysis.EClinicalMedicine · 2026Article
- Machine-Learning-Based Prediction of Preterm Birth in Women with Huge Uterine Fibroids: A Stratified Cohort Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- The Pathophysiology of Spontaneous Preterm Birth: Emerging Mechanisms Reviewed by the Preterm Birth International Collaborative.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Maternal amino acid metabolites during pregnancy and preterm birth: results from two prospective cohort studies.BMC medicine · 2026Article
- Machine learning-based prediction algorithm of spontaneous preterm birth using multi-source data.BMC pregnancy and childbirth · 2025Article
- Discovering latent subtypes of preterm birth and genetic risk using tensor decomposition on electronic health records.medRxiv : the preprint server for health sciences · 2025Article
- Predicting the risk of preterm birth with machine learning and electronic health records in China.BMC medical informatics and decision making · 2025Article
- Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.Seminars in perinatology · 2025Review
- Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.Journal of medical Internet research · 2025Article
- Comorbidities associated with a clinically-recognized delirium diagnosis in the hospital using real world data.Communications medicine · 2025Article
- Predicting preterm birth using machine learning methods.Scientific reports · 2025Article
- Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort.BMC pregnancy and childbirth · 2025Article
- Large language models improve the identification of emergency department visits for symptomatic kidney stones.Scientific reports · 2025Article
- Predicting preterm birth using electronic medical records from multiple prenatal visits.BMC pregnancy and childbirth · 2024Article
- Computational Approaches for Predicting Preterm Birth and Newborn Outcomes.Clinics in perinatology · 2024Review
- Novel insights from our special issue on maternal factors during pregnancy that influence maternal, fetal and childhood outcomes.BMC medicine · 2024Article
- Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data.Frontiers in pediatrics · 2024Article
- Data-driven insights can transform women's reproductive health.npj women's health · 2024Review
- Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
- Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries.Science advances · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundIdentifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy.
methodsHere, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth.
resultsWe find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC = 0.65, PR-AUC = 0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system.
conclusionsBy leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings.
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.