Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies-Reference-Cited by-同舟云学术

Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

Published:2022 Issue:7 Volume:63 Page:692
ISSN:0513-5796
Container-title:Yonsei Medical Journal
language:en
Short-container-title:Yonsei Med J

Author:

Park Tae Jun¹^ORCID,Chang Hye Jin²^ORCID,Choi Byung Jin¹^ORCID,Jung Jung Ah²^ORCID,Kang Seongwoo²^ORCID,Yoon Seokyoung²^ORCID,Kim Miran²³^ORCID,Yoon Dukyong⁴⁵⁶^ORCID

Affiliation:

1. Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.

2. Department of Obstetrics and Gynecology, Ajou University School of Medicine, Suwon, Korea.

3. COSMOSWHALE Inc., Ansan, Korea.

4. Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea.

5. Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea.

6. BUD.on Inc., Jeonju, Korea.

Funder

INNOPOLIS Foundation

Korea Medical Device Development Fund

Publisher

Yonsei University College of Medicine

Subject

General Medicine

Link

https://eymj.org/pdf/10.3349/ymj.2022.63.7.692

Reference29 articles.

1. Electronic fetal monitoring or cardiotocography, 50 years later: what's in a name?

2. Neonatal complications associated with use of fetal scalp electrode: a retrospective study

3. Antepartum Evaluation of the Fetus and Fetal Well Being

4. Monitoring fetal maturation—objectives, techniques and indices of autonomic function

5. Formation of functional associations across time scales in the fetal autonomic control system — A multifractal analysis

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