ArticleBMC pregnancy and childbirth2022
A novel oppositional binary crow search algorithm with optimal machine learning based postpartum hemorrhage prediction model.
Article in BMC pregnancy and childbirth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Accuracy of machine learning and traditional statistical models in the prediction of postpartum haemorrhage: a systematic review.BMJ open · 2025Pooled it
- Establishment of a predictive model for postpartum hemorrhage in twins: a retrospective study.BMC pregnancy and childbirth · 2023Trial
- 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 for postpartum hemorrhage: a systematic review.Frontiers in global women's health · 2026Review
- Establishment of an antepartum predictive model for postpartum hemorrhage in preterm delivery: a retrospective matched case-control study.Frontiers in medicine · 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 in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Artificial Intelligence and Postpartum Hemorrhage.Maternal-fetal medicine (Wolters Kluwer Health, Inc.) · 2025Review
- Quantitative prediction of postpartum hemorrhage in cesarean section on machine learning.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Postpartum hemorrhage (PPH) is an obstetric emergency instigated by excessive blood loss which occurs frequently after the delivery. The PPH can result in volume depletion, hypovolemic shock, and anemia. This is particular condition is considered a major cause of maternal deaths around the globe. Presently, physicians utilize visual examination for calculating blood and fluid loss during delivery. Since the classical methods depend on expert knowledge and are inaccurate, automated machine learning based PPH diagnosis models are essential. In regard to this aspect, this study introduces an efficient oppositional binary crow search algorithm (OBCSA) with an optimal stacked auto encoder (OSAE) model, called OBCSA-OSAE for PPH prediction. The goal of the proposed OBCSA-OSAE technique is to detect and classify the presence or absence of PPH. The OBCSA-OSAE technique involves the design of OBCSA based feature selection (FS) methods to elect an optimum feature subset. Additionally, the OSAE based classification model is developed to include an effective parameter adjustment process utilizing Equilibrium Optimizer (EO). The performance validation of the OBCSA-OSAE technique is performed using the benchmark dataset. The experimental values pointed out the benefits of the OBCSA-OSAE approach in recent methods.
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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.