Machine Learning-Enhanced Surveillance for Surgical Site Infections in Patients Undergoing Colon Surgery: Model Development and Evaluation Study.

ABSTRACT:

Background

Surgical site infections (SSIs) are one of the most common health care-associated infections, accounting for nearly 20% of all health care-associated infections in hospitalized patients. SSIs are associated with longer hospital stays, increased readmission rates, higher health care costs, and a mortality rate twice that of patients without infections.

Objective

This study aimed to develop and evaluate machine learning (ML) models for augmenting SSI surveillance after colon surgery with the goal of improving the efficiency of infection control practices by prioritizing patients at high risk.

Methods

We conducted a retrospective study using data from 1508 patients undergoing colon surgery treated between 2018 and 2023 at a single academic medical center. Of t

SUBMITTER: Celik U 

PROVIDER: S-EPMC12530156 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature