Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments.

ABSTRACT:

Background

Objectives were to build a machine learning algorithm to identify bloodstream infection (BSI) among pediatric patients with cancer and hematopoietic stem cell transplantation (HSCT) recipients, and to compare this approach with presence of neutropenia to identify BSI.

Methods

We included patients 0-18 years of age at cancer diagnosis or HSCT between January 2009 and November 2018. Eligible blood cultures were those with no previous blood culture (regardless of result) within 7 days. The primary outcome was BSI. Four machine learning algorithms were used: elastic net, support vector machine and two implementations of gradient boosting machine (GBM and XGBoost). Model training and evaluation were performed using temporally disjoint training (60%), validation (20%) a

SUBMITTER: Sung L 

PROVIDER: S-EPMC7666525 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature