A Machine Learning-Based Prediction of Hospital Mortality in Patients With Postoperative Sepsis.

ABSTRACT: Introduction: The incidence of postoperative sepsis is continually increased, while few studies have specifically focused on the risk factors and clinical outcomes associated with the development of sepsis after surgical procedures. The present study aimed to develop a mathematical model for predicting the in-hospital mortality among patients with postoperative sepsis. Materials and Methods: Surgical patients in Medical Information Mart for Intensive Care (MIMIC-III) database who simultaneously fulfilled Sepsis 3.0 and Agency for Healthcare Research and Quality (AHRQ) criteria at ICU admission were incorporated. We employed both extreme gradient boosting (XGBoost) and stepwise logistic regression model to predict the in-hospital mortality among patients with postoperative sep

SUBMITTER: Yao RQ 

PROVIDER: S-EPMC7438711 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature