Predicting postoperative surgical site infection with administrative data: a random forests algorithm

BACKGROUND: Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health...

Descripción completa

Detalles Bibliográficos
Autores principales: Petrosyan, Yelena, Thavorn, Kednapa, Smith, Glenys, Maclure, Malcolm, Preston, Roanne, van Walravan, Carl, Forster, Alan J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403439/
https://www.ncbi.nlm.nih.gov/pubmed/34454414
http://dx.doi.org/10.1186/s12874-021-01369-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403439/
https://www.ncbi.nlm.nih.gov/pubmed/34454414
http://dx.doi.org/10.1186/s12874-021-01369-9

Ejemplares similares