Detection of clinically important colorectal surgical site infection using Bayesian network.

ABSTRACT:

Background

Despite extensive efforts to monitor and prevent surgical site infections (SSIs), real-time surveillance of clinical practice has been sparse and expensive or nonexistent. However, natural language processing (NLP) and machine learning (i.e., Bayesian network analysis) may provide the methodology necessary to approach this issue in a new way. We investigated the ability to identify SSIs after colorectal surgery (CRS) through an automated detection system using a Bayesian network.

Materials and methods

Patients who underwent CRS from 2010 to 2012 and were captured in our institutional American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) comprised our cohort. A Bayesian network was applied to detect SSIs using risk factors from ACS-

SUBMITTER: Sohn S 

PROVIDER: S-EPMC5391146 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature