Monitoring for early prediction of gram-negative bacteremia using machine learning and hematological data in the emergency department.

ABSTRACT:

Background

This study aims to develop an artificial intelligence-assisted tool for the prediction of Gram-negative bacteremia, using cell population data, complete blood count, and differential count. The model seeks to distinguish among nonbacteremia, Gram-negative bacteremia, and Gram-positive bacteremia in patients presenting to the emergency department.

Methods

This retrospective study was conducted in the emergency departments of three hospitals in Taiwan. Data from adults with suspected bacterial infections were collected, including complete blood count, white blood cell differential count, and cell population data. A gradient boosting model (Catboost) was developed to classify nonbacteremia, Gram-negative and Gram-positive bacteremia. We evaluated the model through di

SUBMITTER: Chiu YW 

PROVIDER: S-EPMC12630726 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature