Real-time Classification of Fetal Status Based on Deep Learning and Cardiotocography Data-Reference-Cited by-同舟云学术

Real-time Classification of Fetal Status Based on Deep Learning and Cardiotocography Data

Published:2023-08-03 Issue:1 Volume:47 Page:
ISSN:1573-689X
Container-title:Journal of Medical Systems
language:en
Short-container-title:J Med Syst

Author:

Lee Kwang-Sig,Choi Eun Saem,Nam Young Jin,Liu Nae Won,Yang Yong Seok,Kim Ho Yeon,Ahn Ki Hoon,Hong Soon Cheol

Funder

Korea Health Industry Development Institute grant (Korea Health Technology R&D Project) funded by the Ministry of Health and Welfare of South Korea

IITP (Institute of Information & Communications Technology Planning & Evaluation) grant funded by the Ministry of Science and ICT of South Korea

PACEN (Patient-Centered Clinical Research Coordinating Center) grant funded by the Ministry of Health & Welfare of South Korea

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s10916-023-01960-1.pdf

Reference30 articles.

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2. Cleveland Clinic. Health library: high-risk pregnancy. 2022. Available online: https://my.clevelandclinic.org/health/diseases/22190-high-risk-pregnancy (accessed on 1 June 2022).

3. Mayo Clinic. Healthy lifestyle: high-risk pregnancy. 2022. Available online: https://www.mayoclinic.org/healthy-lifestyle/pregnancy-week-by-week/in-depth/high-risk-pregnancy/art-20047012 (accessed on 1 June 2022).

4. Mathews TJ, Driscoll AK. Trends in infant mortality in the United States, 2005–2014. NCHS data brief, no 279. Hyattsville, MD: National Center for Health Statistics. 2017.

5. American College of Obstetricians and Gynecologists’ Committee on Practice Bulletins—Obstetrics. Antepartum Fetal Surveillance: ACOG Practice Bulletin, Number 229. Obstet Gynecol. 2021 Jun 1;137(6):e116-e127.

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