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...
| Publicado en: | BMC Medical Research Methodology Vol. 21; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
8/28/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |