Unbiased identification of risk factors for invasive Escherichia coli disease using machine learning.

ABSTRACT:

Background

Invasive Escherichia coli disease (IED), also known as invasive extraintestinal pathogenic E. coli disease, is a leading cause of sepsis and bacteremia in older adults that can result in hospitalization and sometimes death and is frequently associated with antimicrobial resistance. Moreover, certain patient characteristics may increase the risk of developing IED. This study aimed to validate a machine learning approach for the unbiased identification of potential risk factors that correlate with an increased risk for IED.

Methods

Using electronic health records from 6.5 million people, an XGBoost model was trained to predict IED from 663 distinct patient features, and the most predictive features were identified as potential risk factors. Using Shapley Additive pr

SUBMITTER: Clarke E 

PROVIDER: S-EPMC11308465 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature