Journal article

Identifying High-Risk Pre-Term Pregnancies Using the Fetal Heart Rate and Machine Learning

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Davis Jones, G., Cooke, W. R., & Vatish, M. (2026). Identifying High-Risk Pre-Term Pregnancies Using the Fetal Heart Rate and Machine Learning. Bioengineering, 13(2).

MLA Style

Davis Jones, G, et al. “Identifying High-Risk Pre-Term Pregnancies Using the Fetal Heart Rate and Machine Learning.” Bioengineering, vol. 13, no. 2, 2026.

Chicago Style

Davis Jones, G, WR Cooke, and M Vatish. 2026. “Identifying High-Risk Pre-Term Pregnancies Using the Fetal Heart Rate and Machine Learning.” Bioengineering 13 (2).
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Files:: Davis_Jones_et_al_2026_Identifying_High-Risk_Pre-Term.pdf

(Preview, Version of record, pdf, 341.3KB, Terms of use)

Publisher copy:: 10.3390/bioengineering13020203

Authors

+ Davis Jones, G More by this author

Institution:: University of Oxford
Role:: Author

+ Cooke, WR More by this author

Institution:: University of Oxford
Role:: Author

+ Vatish, M More by this author

Institution:: University of Oxford
Role:: Author

Publisher:: MDPI
Journal:: Bioengineering More from this journal
Volume:: 13
Issue:: 2
Publication date:: 2026-02-01
Acceptance date:: 2026-02-03
DOI:: 10.3390/bioengineering13020203
EISSN:: 2306-5354
ISSN:: 2306-5354

Language:: English
Keywords:: Cardiotocography

Antepartum Surveillance

Perinatal Outcomes

Pre-term Birth

Risk stratification

Machine Learning

Fetal Heart Rate Monitoring
Pubs id:: 2374660
Local pid:: pubs:2374660
Source identifiers:: 3829638
Deposit date:: 2026-03-07
ARK identifier:: ark:/29072/ora_8c3c9cf6e0e841dba5d6b60cb5f79553

Terms of use

Copyright date:: 2026

Licence:: CC Attribution (CC BY)

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