Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms
A simplified machine learning model for predicting late FGR may be useful for evaluating individual risks in the early third trimester.
Authors
- Kicheol Kil (ORCID: https://orcid.org/0000-0001-5685-6893)
- Seon Ui Lee
- Hyun Sun Ko (ORCID: https://orcid.org/0000-0001-6310-6206)
- Jae Eun Shin (ORCID: https://orcid.org/0000-0002-8326-8541)
- Yun Sung Jo (ORCID: https://orcid.org/0000-0002-5618-9338)
- Jeong Ha Wie (ORCID: https://orcid.org/0000-0001-7897-5148)
- Yeon Hee Kim (ORCID: https://orcid.org/0000-0002-7689-3840)
- Sae Kyung Choi
Institutions
- The Catholic University of Korea St. Vincent's Hospital (KR)
- The Catholic University of Korea Incheon St. Mary's Hospital (KR)
- The Catholic University of Korea Uijeongbu St. Mary's Hospital (KR)
- The Catholic University of Korea Yeouido St. Mary's Hospital (KR)
- The Catholic University of Korea Bucheon St. Mary's Hospital (KR)
- The Catholic University of Korea Seoul St. Mary's Hospital (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Life
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3390/life14111521
- Citations
- 5
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 2.91