Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.

ABSTRACT:

Background

Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO proliferation. Timely identification of patients at high risk for MDRO can aid in curbing transmission, enhancing patient outcomes, and maintaining the cleanliness of the ICU environment. This study focused on developing a machine learning (ML) model to identify patients at risk of MDRO during the initial phase of their ICU stay.

Methods

Utilizing patient data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH-ICU) and the Medical Information Mart for Intensive Care (MIMIC-IV), the study analyzed

SUBMITTER: Li Y 

PROVIDER: S-EPMC11227715 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature