Predicting mortality risk for preterm infants using random forest
Abstract Mortality is an unfortunately common outcome of extremely and very preterm birth. Existing clinical prediction models capture mortality risk at birth but fail to account for the remainder of the hospital course. Infants born < 32 weeks gestation with complete physiologic and clinical dat...
Guardado en:
Autores principales: | Jennifer Lee, Jinjin Cai, Fuhai Li, Zachary A. Vesoulis |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/5f2c133598bb4e5e884623873be2e4dc |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
por: Jiarui Feng, et al.
Publicado: (2021) -
Neonatal Morbidity and Mortality in Advanced Aged Mothers—Maternal Age Is Not an Independent Risk Factor for Infants Born Very Preterm
por: Nasenien Nourkami-Tutdibi, et al.
Publicado: (2021) -
Risk factors for the deterioration of periventricular–intraventricular hemorrhage in preterm infants
por: Tian Wu, et al.
Publicado: (2020) -
Antibiotics and the developing intestinal microbiome, metabolome and inflammatory environment in a randomized trial of preterm infants
por: Jordan T. Russell, et al.
Publicado: (2021) -
Mortality and neurological outcomes in extremely and very preterm infants born to mothers with hypertensive disorders of pregnancy
por: Noriyuki Nakamura, et al.
Publicado: (2021)