Comparison between machine learning methods for mortality prediction for sepsis patients with different social determinants.

ABSTRACT:

Background

Sepsis is one of the most life-threatening circumstances for critically ill patients in the United States, while diagnosis of sepsis is challenging as a standardized criteria for sepsis identification is still under development. Disparities in social determinants of sepsis patients can interfere with the risk prediction performances using machine learning.

Methods

We analyzed a cohort of critical care patients from the Medical Information Mart for Intensive Care (MIMIC)-III database. Disparities in social determinants, including race, sex, marital status, insurance types and languages, among patients identified by six available sepsis criteria were revealed by forest plots with 95% confidence intervals. Sepsis patients were then identified by the Sepsis-3 criteria

SUBMITTER: Wang H 

PROVIDER: S-EPMC9204861 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature