A machine-learning model for prediction of Acinetobacter baumannii hospital acquired infection.

ABSTRACT:

Background

Acinetobacter baumanni infection is a leading cause of morbidity and mortality in the Intensive Care Unit (ICU). Early recognition of patients at risk for infection allows early proper treatment and is associated with improved outcomes. This study aimed to construct an innovative Machine Learning (ML) based prediction tool for Acinetobacter baumanni infection, among patients in the ICU, and to examine its robustness and predictive power.

Methods

For model development and internal validation, we used The Medical Information Mart for Intensive Care database (MIMIC) III data from 19,690 consecutive adult patients admitted between 2001 and 2012 at a Boston tertiary center ICU. For external validation, we used a different dataset from Rabin Medical Center (RMC, Israeli

SUBMITTER: Neuman I 

PROVIDER: S-EPMC11620386 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature