Machine learning-based modeling of acute respiratory failure following emergency general surgery operations.

ABSTRACT:

Background

Emergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored.

Objective

The present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to traditional regression models using a nationally-representative cohort.

Methods

Non-elective hospitalizations for EGS (appendectomy, cholecystectomy, repair of perforated ulcer, large or small bowel resection, lysis of adhesions) were identified in the 2016-18 Nationwide Readmissions Databas

SUBMITTER: Hadaya J 

PROVIDER: S-EPMC9049563 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature