A filter-predictor polynomial feature based machine learning approach to predicting preterm birth from cervical electrical impedance spectroscopy-Reference-Cited by-同舟云学术

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A filter-predictor polynomial feature based machine learning approach to predicting preterm birth from cervical electrical impedance spectroscopy

Published:2023-02 Issue: Volume:80 Page:104345
ISSN:1746-8094
Container-title:Biomedical Signal Processing and Control
language:en
Short-container-title:Biomedical Signal Processing and Control

Author:

Tian David,Lang Zi-Qiang,Zhang Di,Anumba Dilly O.

Funder

Medical Research Council

Engineering and Physical Sciences Research Council

Publisher

Elsevier BV

Subject

Health Informatics,Signal Processing,Biomedical Engineering

Reference40 articles.

1. Predictive accuracy of serial transvaginal cervical lengths and quantitative vaginal fetal fibronectin levels for spontaneous preterm birth among nulliparous women;Esplin;JAMA,2017

2. The preterm prediction study: can low-risk women destined for spontaneous preterm birth be identified?;Iams;Am. J. Obstet. Gynecol.,2001

3. The capacity of mid-pregnancy cervical length to predict preterm birth in low-risk women: a national cohort study;Van;Acta Obstet. Gynecol. Scand.,2015

4. Serial transvaginal cervical length measurements and quantitative vaginal fetal fibronectin concentrations did not predict spontaneous preterm birth in low-risk nulliparous women;Dudley;Evid. Based Med.,2017

5. Predictiing risk of stillbirth and preterm pregnancies with machine learning;Koivu;Health Inf. Sci. Syst.,2020

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