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Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: a study protocol

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dc.contributor.author Hoodbhoy, Zahra
dc.contributor.author Hasan, Babar
dc.contributor.author Jehan, Fyezah
dc.contributor.author Bijnens, Bart
dc.contributor.author Chowdhury, Devyani
dc.date.accessioned 2022-10-17T06:14:48Z
dc.date.available 2022-10-17T06:14:48Z
dc.date.issued 2018
dc.identifier.citation Hoodbhoy Z, Hasan B, Jehan F, Bijnens B, Chowdhury D. Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: a study protocol. Gates Open Research. 2018 Feb;2(8):1-13. DOI: 10.12688/gatesopenres.12796.1
dc.identifier.issn 2572-4754
dc.identifier.uri http://hdl.handle.net/10230/54410
dc.description.abstract In Pakistan, stillbirth rates and early neonatal mortality rates are amongst the highest in the world. The aim of this study is to provide proof of concept for using a computational model of fetal haemodynamics, combined with machine learning. This model will be based on Doppler patterns of the fetal cardiovascular, cerebral and placental flows with the goal to identify those fetuses at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities. This will be prospective one group cohort study which will be conducted in Ibrahim Hyderi, a peri-urban settlement in south east of Karachi. The eligibility criteria include pregnant women between 22-34 weeks who reside in the study area. Once enrolled, in addition to the performing fetal ultrasound to obtain Dopplers, data on sociodemographic, maternal anthropometry, haemoglobin and cardiotocography will be obtained on the pregnant women. The machine learning approach for predicting adverse perinatal outcomes obtained from the current study will be validated in a larger population at the next stage. The data will allow for early interventions to improve perinatal outcomes.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher F1000Research
dc.relation.ispartof Gates Open Research. 2018 Feb;2(8):1-13
dc.rights © 2018 Hoodbhoy Z et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: a study protocol
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.12688/gatesopenres.12796.1
dc.subject.keyword Echocardiography
dc.subject.keyword Pregnancy
dc.subject.keyword Machine learning
dc.subject.keyword Adverse outcomes
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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