Cargando…
Development and Validation of an Integrated Suite of Prediction Models for All-Cause 30-Day Readmissions of Children and Adolescents Aged 0 to 18 Years
IMPORTANCE: Readmission is often considered a hospital quality measure, yet no validated risk prediction models exist for children. OBJECTIVE: To develop and validate a tool identifying patients before hospital discharge who are at risk for subsequent readmission, applicable to all ages. DESIGN, SET...
Autores principales: | Goodman, Denise M., Casale, Mia T., Rychlik, Karen, Carroll, Michael S., Auger, Katherine A., Smith, Tracie L., Cartland, Jenifer, Davis, Matthew M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Association
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652755/ https://www.ncbi.nlm.nih.gov/pubmed/36367725 http://dx.doi.org/10.1001/jamanetworkopen.2022.41513 |
Ejemplares similares
-
Neural networks versus Logistic regression for 30 days all-cause readmission prediction
por: Allam, Ahmed, et al.
Publicado: (2019) -
Epidemiology and Predictors of all-cause 30-Day readmission in patients with sickle cell crisis
por: Kumar, Vivek, et al.
Publicado: (2020) -
Comparison of predictive modeling approaches for 30-day all-cause non-elective readmission risk
por: Tong, Liping, et al.
Publicado: (2016) -
Predicting all-cause risk of 30-day hospital readmission using artificial neural networks
por: Jamei, Mehdi, et al.
Publicado: (2017) -
Prediction of 30-Day Hospital Readmissions for All-Cause Dental Conditions using Machine Learning
por: Hung, Man, et al.
Publicado: (2020)