Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration

Autor:	Brenda F. Narice, Mariam Labib, Mengxiao Wang, Victoria Byrne, Joanna Shepherd, Z. Q. Lang, Dilly OC Anumba
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Preterm birth Pregnancy Prediction Logistic regression model Machine learning Gynecology and obstetrics RG1-991
Zdroj:	BMC Pregnancy and Childbirth, Vol 24, Iss 1, Pp 1-10 (2024)
Druh dokumentu:	article
ISSN:	1471-2393
DOI:	10.1186/s12884-024-06892-3
Popis:	Abstract Background Current predictive machine learning techniques for spontaneous preterm birth heavily rely on a history of previous preterm birth and/or costly techniques such as fetal fibronectin and ultrasound measurement of cervical length to the disadvantage of those considered at low risk and/or those who have no access to more expensive screening tools. Aims and objectives We aimed to develop a predictive model for spontaneous preterm delivery
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/588535050e444d13ad37da4ee77af7bf 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.