Machine Learning-Based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.

ABSTRACT:

Objectives

This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in pediatric patients with cancer who presenting febrile neutropenia (FN) at emergency department (ED) visit.

Materials and methods

A retrospective study was conducted on patients, aged younger than 18 years, who visited a tertiary university-affiliated hospital ED due to FN between January 2004 and August 2022. ML models, based on XGBoost, were developed for BSI and SS prediction.

Results

After applying the exclusion criteria, we identified 4423 FN events during the study period. We identified 195 (4.4%) BSI and 107 (2.4%) SS events. The BSI and SS models demonstrated promising performance, with area under the receiver opera

SUBMITTER: Park JS 

PROVIDER: S-EPMC11676618 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature