ArticleResearch in nursing & health2021
Three machine learning algorithms and their utility in exploring risk factors associated with primary cesarean section in low-risk women: A methods paper.
Article in Research in nursing & health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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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.
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Who cites it
7 citing papers in PubMed.
- Cohort Study Summary of the Effects of Carboprost Tromethamine Combined with Oxytocin on Infant Outcome, Postpartum Hemorrhage and Uterine Involution of Parturients Undergoing Cesarean Section.Computational and mathematical methods in medicine · 2022Trial
- JAK-STAT and IL-17 pathway dysregulation underlies persistent immune dysfunction in ART-experienced people living with HIV in Ghana.Frontiers in immunology · 2026Article
- A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital.BMC medical informatics and decision making · 2025Article
- Divergent effects of sleep efficiency and sleep medication on episodic memory in mid to late life.Frontiers in sleep · 2025Article
- Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach.International journal of environmental research and public health · 2023Article
- Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications.Frontiers in endocrinology · 2023Review
- A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in health research. The purpose of this methods paper is to describe three machine learning algorithms in detail and provide an example of their use in maternal outcomes research. The three algorithms, classification and regression trees, least absolute shrinkage and selection operator, and random forest, may be used to understand risk groups, select variables for a model, and rank variables' contribution to an outcome, respectively. While machine learning has plenty to contribute to health research, it also has some drawbacks, and these are discussed as well. To provide an example of the different algorithms' function, they were used on a completed cross-sectional study examining the association of oxytocin total dose exposure with primary cesarean section. The results of the algorithms are compared to what was done or found using more traditional methods.
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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.