Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea

This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor. Nine machine learning methods of logistic regression, random...

Descripción completa

Detalles Bibliográficos
Autores principales: Wie, Jeong Ha, Lee, Se Jin, Choi, Sae Kyung, Jo, Yun Sung, Hwang, Han Sung, Park, Mi Hye, Kim, Yeon Hee, Shin, Jae Eun, Kil, Ki Cheol, Kim, Su Mi, Choi, Bong Suk, Hong, Hanul, Seol, Hyun-Joo, Won, Hye-Sung, Ko, Hyun Sun, Na, Sunghun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033083/
https://www.ncbi.nlm.nih.gov/pubmed/35455095
http://dx.doi.org/10.3390/life12040604

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033083/
https://www.ncbi.nlm.nih.gov/pubmed/35455095
http://dx.doi.org/10.3390/life12040604

Ejemplares similares