Fetal heart rate changes and labor neuraxial analgesia: a machine learning approach

Autor:	Efrain Riveros-Perez, Javier Jose Polania-Gutierrez, Bibiana Avella-Molano
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Fetal heart rate Anesthesia and Analgesia Machine learning Gynecology and obstetrics RG1-991
Zdroj:	BMC Pregnancy and Childbirth, Vol 23, Iss 1, Pp 1-7 (2023)
Druh dokumentu:	article
ISSN:	1471-2393
DOI:	10.1186/s12884-023-05632-3
Popis:	Abstract Background Neuraxial labor analgesia has been associated with fetal heart rate changes. Fetal bradycardia is multifactorial, and predicting it poses a significant challenge to clinicians. Machine learning algorithms may assist the clinician to predict fetal bradycardia and identify predictors associated with its presentation. Methods A retrospective analysis of 1077 healthy laboring parturients receiving neuraxial analgesia was conducted. We compared a principal components regression model with tree-based random forest, ridge regression, multiple regression, a general additive model, and elastic net in terms of prediction accuracy and interpretability for inference purposes. Results Multiple regression identified combined spinal-epidural (CSE) (p = 0.02), interaction between CSE and dose of phenylephrine (p
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/dd55d1fc63c346a6ab646b9d75ae402a Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.