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

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

Abstract 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...

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Bibliographic Details
Main Authors: Yelena Petrosyan, Kednapa Thavorn, Glenys Smith, Malcolm Maclure, Roanne Preston, Carl van Walravan, Alan J. Forster
Format: Article
Language:English
Published: BMC 2021-08-01
Series:BMC Medical Research Methodology
Subjects:
Surgical site infection
Administrative data
Machine learning
Random forests
Data mining
Predictive modeling
Online Access:https://doi.org/10.1186/s12874-021-01369-9

Internet

https://doi.org/10.1186/s12874-021-01369-9

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