Development and validation of models for detection of postoperative infections using structured electronic health records data and machine learning.

ABSTRACT:

Background

Postoperative infections constitute more than half of all postoperative complications. Surveillance of these complications is primarily done through manual chart review, which is time consuming, expensive, and typically only covers 10% to 15% of all operations. Automated surveillance would permit the timely evaluation of and reporting of all operations.

Methods

The goal of this study was to develop and validate parsimonious, interpretable models for conducting surveillance of postoperative infections using structured electronic health records data. This was a retrospective study using 30,639 unique operations from 5 major hospitals between 2013 and 2019. Structured electronic health records data were linked to postoperative outcomes data from the American College

SUBMITTER: Colborn KL 

PROVIDER: S-EPMC10204069 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature