ArticleJournal of the American Medical Informatics Association : JAMIA2022
Improving postpartum hemorrhage risk prediction using longitudinal electronic medical records.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. 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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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of machine learning and traditional statistical models in the prediction of postpartum haemorrhage: a systematic review.BMJ open · 2025Pooled it
- Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review.Epidemiologia (Basel, Switzerland) · 2026Review
- AI-driven high-risk pregnancy prediction: balancing early detection, anxiety, and discrimination in digital public health.Frontiers in public health · 2026Article
- Development and validation of a postpartum hemorrhage risk prediction model for advanced maternal age women undergoing vaginal delivery: a comparison of multiple machine learning algorithms.Frontiers in medicine · 2026Article
- Artificial Intelligence and Machine Learning for predicting Major Obstetric Emergencies: Current Evidence, Clinical Translation, and Future Directions.International journal of women's health · 2026Review
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 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
- Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births.BMC pregnancy and childbirth · 2025Article
- Artificial Intelligence and Postpartum Hemorrhage.Maternal-fetal medicine (Wolters Kluwer Health, Inc.) · 2025Review
- Development of antepartum risk prediction model for postpartum hemorrhage in Lagos, Nigeria: A prospective cohort study (Predict-PPH study).International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2024Article
- An algorithm to identify patients aged 0-3 with rare genetic disorders.Orphanet journal of rare diseases · 2024Article
- Prediction of postpartum hemorrhage using traditional statistical analysis and a machine learning approach.AJOG global reports · 2023Article
- Article
- Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population.Frontiers in global women's health · 2023Article
- Development and Validation of a Predictive Tool for Postpartum Hemorrhage after Vaginal Delivery: A Prospective Cohort Study.Biology · 2022Article
- Risk-factor model for postpartum hemorrhage after cesarean delivery: a retrospective study based on 3498 patients.Scientific reports · 2022Article
- Late preterm antenatal corticosteroids in singleton and twin gestations: a retrospective cohort study.BMC pregnancy and childbirth · 2022Article
- Digital health and telemedicine in Pakistan: Improving maternal healthcare.Annals of medicine and surgery (2012) · 2022Article
- A novel oppositional binary crow search algorithm with optimal machine learning based postpartum hemorrhage prediction model.BMC pregnancy and childbirth · 2022Article
- Informatics for sex- and gender-related health: understanding the problems, developing new methods, and designing new solutions.Journal of the American Medical Informatics Association : JAMIA · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectivePostpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality in the United States. We sought to develop a novel risk assessment tool and compare its accuracy to tools used in current practice. MATERIALS AND
methodsWe used a PPH digital phenotype that we developed and validated previously to identify 6639 PPH deliveries from our delivery cohort (N = 70 948). Using a vast array of known and potential risk factors extracted from electronic medical records available prior to delivery, we trained a gradient boosting model in a subset of our cohort. In a held-out test sample, we compared performance of our model with 3 clinical risk-assessment tools and 1 previously published model.
resultsOur 24-feature model achieved an area under the receiver-operating characteristic curve (AUROC) of 0.71 (95% confidence interval [CI], 0.69-0.72), higher than all other tools (research-based AUROC, 0.67 [95% CI, 0.66-0.69]; clinical AUROCs, 0.55 [95% CI, 0.54-0.56] to 0.61 [95% CI, 0.59-0.62]). Five features were novel, including red blood cell indices and infection markers measured upon admission. Additionally, we identified inflection points for vital signs and labs where risk rose substantially. Most notably, patients with median intrapartum systolic blood pressure above 132 mm Hg had an 11% (95% CI, 8%-13%) median increase in relative risk for PPH.
conclusionsWe developed a novel approach for predicting PPH and identified clinical feature thresholds that can guide intrapartum monitoring for PPH risk. These results suggest that our model is an excellent candidate for prospective evaluation and could ultimately reduce PPH morbidity and mortality through early detection and prevention.
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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.