On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines

Journal article


Nsugbe, E., Reyes-Lagos, J.J., Adams, D. and Samuel, O. 2023. On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines. Healthcare Technology Letters. 10 (1-2), pp. 11-22. https://doi.org/10.1049/htl2.12044
AuthorsNsugbe, E., Reyes-Lagos, J.J., Adams, D. and Samuel, O.
Abstract

Preterm birth is a global epidemic affecting millions of mothers across different ethnicities. The cause of the condition remains unknown but has recognised health-based implications, in addition to financial and economic ones. Machine Learning methods have enabled researchers to combine datasets using uterine contraction signals with various forms of prediction machines to improve awareness of the likelihood of premature births. This work investigates the feasibility of enhancing these prediction methods using physiological signals including uterine contractions, and foetal and maternal heart rate signals, for a population of South American women in active labour. As part of this work, the use of the Linear Series Decomposition Learner (LSDL) was seen to lead to an improvement in the prediction accuracies of all models, which included supervised and unsupervised learning models. The results from the supervised learning models showed high prediction metrics upon the physiological signals being pre-processed by the LSDL for all variations of the physiological signals. The unsupervised learning models showed good metrics for the partitioning of Preterm/Term labour patients from their uterine contraction signals but produced a comparatively lower set of results for the various kinds of heart rate signals investigated.

KeywordsPremature Birth Prediction; Machine Learning; Linear Series Decomposition Learner; Uterine Contractions
Year2023
JournalHealthcare Technology Letters
Journal citation10 (1-2), pp. 11-22
PublisherWiley
ISSN20533713
Digital Object Identifier (DOI)https://doi.org/10.1049/htl2.12044
Web address (URL)https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/htl2.12044
Accepted author manuscript
File Access Level
Open
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online08 Apr 2023
Publication process dates
Accepted23 Mar 2023
Deposited22 Jun 2023
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