Prediction of short-term health outcomes in preterm neonates from heart-rate variability and blood pressure using boosted decision trees

dc.check.date2020-07-26
dc.check.infoAccess to this article is restricted until 12 months after publication by request of the publisher.en
dc.contributor.authorSemenova, Oksana
dc.contributor.authorCarra, Giorgia
dc.contributor.authorLightbody, Gordon
dc.contributor.authorBoylan, Geraldine B.
dc.contributor.authorDempsey, Eugene M.
dc.contributor.authorTemko, Andriy
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2019-09-09T10:42:09Z
dc.date.available2019-09-09T10:42:09Z
dc.date.issued2019-07-26
dc.date.updated2019-08-01T11:28:06Z
dc.description.abstractBackground and Objective: Efficient management of low blood pressure (BP) in preterm neonates remains challenging with considerable variability in clinical practice. There is currently no clear consensus on what constitutes a limit for low BP that is a risk to the preterm brain. It is argued that a personalised approach rather than a population based threshold is more appropriate. This work aims to assist healthcare professionals in assessing preterm wellbeing during episodes of low BP in order to decide when and whether hypotension treatment should be initiated. In particular, the study investigates the relationship between heart rate variability (HRV) and BP in preterm infants and its relevance to a short-term health outcome. Methods: The study is performed on a large clinically collected dataset of 831 h from 23 preterm infants of less than 32 weeks gestational age. The statistical predictive power of common HRV features is first assessed with respect to the outcome. A decision support system, based on boosted decision trees (XGboost), was developed to continuously estimate the probability of neonatal morbidity based on the feature vector of HRV characteristics and the mean arterial blood pressure. Results: It is shown that the predictive power of the extracted features improves when observed during episodes of hypotension. A single best HRV feature achieves an AUC of 0.87. Combining multiple HRV features extracted during hypotensive episodes with the classifier achieves an AUC of 0.97, using a leave-one-patient-out performance assessment. Finally it is shown that good performance can even be achieved using continuous HRV recordings, rather than only focusing on hypotensive events – this had the benefit of not requiring invasive BP monitoring. Conclusions: The work presents a promising step towards the use of multimodal data in providing objective decision support for the prediction of short-term outcome in preterm infants with hypotensive episodes.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid104996en
dc.identifier.citationSemenova, O., Carra, G., Lightbody, G., Boylan, G., Dempsey, E. and Temko, A. (2019) 'Prediction of short-term health outcomes in preterm neonates from heart-rate variability and blood pressure using boosted decision trees', Computer Methods and Programs in Biomedicine, 180, 104996 (13pp). doi: 10.1016/j.cmpb.2019.104996en
dc.identifier.doi10.1016/j.cmpb.2019.104996en
dc.identifier.eissn1872-7565
dc.identifier.endpage13en
dc.identifier.issn0169-2607
dc.identifier.journaltitleComputer Methods and Programs in Biomedicineen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/8471
dc.identifier.volume180en
dc.language.isoenen
dc.publisherElsevier B.V.en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2272/IE/Irish Centre for Fetal and Neonatal Translational Research (INFANT)/en
dc.relation.urihttp://www.sciencedirect.com/science/article/pii/S0169260719304353
dc.rights© 2019, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 licence.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectHypotensionen
dc.subjectHRVen
dc.subjectBoosted decision treeen
dc.subjectOutcome predictionen
dc.titlePrediction of short-term health outcomes in preterm neonates from heart-rate variability and blood pressure using boosted decision treesen
dc.typeArticle (peer-reviewed)en
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