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 health administrative data, using Machine Learning algorithms.

Methods

All patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk s

SUBMITTER: Petrosyan Y 

PROVIDER: S-EPMC8403439 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature