Accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva

Abstract Steroidal hormone interaction in pregnancy is crucial for adequate fetal evolution and preparation for childbirth and extrauterine life. Estrone sulphate, estriol, progesterone and cortisol play important roles in the initiation of labour mechanism at the start of contractions and cervical...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Silvia Alonso, Sara Cáceres, Daniel Vélez, Luis Sanz, Gema Silvan, Maria Jose Illera, Juan Carlos Illera
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7f0a5d086ef2498993702d61ae88f09d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7f0a5d086ef2498993702d61ae88f09d
record_format	dspace
spelling	oai:doaj.org-article:7f0a5d086ef2498993702d61ae88f09d2021-12-02T13:20:04ZAccurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva10.1038/s41598-021-84924-02045-2322https://doaj.org/article/7f0a5d086ef2498993702d61ae88f09d2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84924-0https://doaj.org/toc/2045-2322Abstract Steroidal hormone interaction in pregnancy is crucial for adequate fetal evolution and preparation for childbirth and extrauterine life. Estrone sulphate, estriol, progesterone and cortisol play important roles in the initiation of labour mechanism at the start of contractions and cervical effacement. However, their interaction remains uncertain. Although several studies regarding the hormonal mechanism of labour have been reported, the prediction of date of birth remains a challenge. In this study, we present for the first time machine learning algorithms for the prediction of whether spontaneous labour will occur from week 37 onwards. Estrone sulphate, estriol, progesterone and cortisol were analysed in saliva samples collected from 106 pregnant women since week 34 by enzyme-immunoassay (EIA) techniques. We compared a random forest model with a traditional logistic regression over a dataset constructed with the values observed of these measures. We observed that the results, evaluated in terms of accuracy and area under the curve (AUC) metrics, are sensibly better in the random forest model. For this reason, we consider that machine learning methods contribute in an important way to the obstetric practice.Silvia AlonsoSara CáceresDaniel VélezLuis SanzGema SilvanMaria Jose IlleraJuan Carlos IlleraNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Silvia Alonso Sara Cáceres Daniel Vélez Luis Sanz Gema Silvan Maria Jose Illera Juan Carlos Illera Accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva
description	Abstract Steroidal hormone interaction in pregnancy is crucial for adequate fetal evolution and preparation for childbirth and extrauterine life. Estrone sulphate, estriol, progesterone and cortisol play important roles in the initiation of labour mechanism at the start of contractions and cervical effacement. However, their interaction remains uncertain. Although several studies regarding the hormonal mechanism of labour have been reported, the prediction of date of birth remains a challenge. In this study, we present for the first time machine learning algorithms for the prediction of whether spontaneous labour will occur from week 37 onwards. Estrone sulphate, estriol, progesterone and cortisol were analysed in saliva samples collected from 106 pregnant women since week 34 by enzyme-immunoassay (EIA) techniques. We compared a random forest model with a traditional logistic regression over a dataset constructed with the values observed of these measures. We observed that the results, evaluated in terms of accuracy and area under the curve (AUC) metrics, are sensibly better in the random forest model. For this reason, we consider that machine learning methods contribute in an important way to the obstetric practice.
format	article
author	Silvia Alonso Sara Cáceres Daniel Vélez Luis Sanz Gema Silvan Maria Jose Illera Juan Carlos Illera
author_facet	Silvia Alonso Sara Cáceres Daniel Vélez Luis Sanz Gema Silvan Maria Jose Illera Juan Carlos Illera
author_sort	Silvia Alonso
title	Accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva
title_short	Accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva
title_full	Accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva
title_fullStr	Accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva
title_full_unstemmed	Accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva
title_sort	accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7f0a5d086ef2498993702d61ae88f09d
work_keys_str_mv	AT silviaalonso accuratepredictionofbirthimplementingastatisticalmodelthroughthedeterminationofsteroidhormonesinsaliva AT saracaceres accuratepredictionofbirthimplementingastatisticalmodelthroughthedeterminationofsteroidhormonesinsaliva AT danielvelez accuratepredictionofbirthimplementingastatisticalmodelthroughthedeterminationofsteroidhormonesinsaliva AT luissanz accuratepredictionofbirthimplementingastatisticalmodelthroughthedeterminationofsteroidhormonesinsaliva AT gemasilvan accuratepredictionofbirthimplementingastatisticalmodelthroughthedeterminationofsteroidhormonesinsaliva AT mariajoseillera accuratepredictionofbirthimplementingastatisticalmodelthroughthedeterminationofsteroidhormonesinsaliva AT juancarlosillera accuratepredictionofbirthimplementingastatisticalmodelthroughthedeterminationofsteroidhormonesinsaliva
_version_	1718393220331732992

Accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva

Ejemplares similares