Using machine learning for the personalised prediction of revision endoscopic sinus surgery.

ABSTRACT:

Background

Revision endoscopic sinus surgery (ESS) is often considered for chronic rhinosinusitis (CRS) if maximal conservative treatment and baseline ESS prove insufficient. Emerging research outlines the risk factors of revision ESS. However, accurately predicting revision ESS at the individual level remains uncertain. This study aims to examine the prediction accuracy of revision ESS and to identify the effects of risk factors at the individual level.

Methods

We collected demographic and clinical variables from the electronic health records of 767 surgical CRS patients ≥16 years of age. Revision ESS was performed on 111 (14.5%) patients. The prediction accuracy of revision ESS was examined by training and validating different machine learning models, while the effects of

SUBMITTER: Nuutinen M 

PROVIDER: S-EPMC9053825 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature