Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study.

ABSTRACT:

Background

Appropriate empirical treatment for candidemia is associated with reduced mortality; however, the timely diagnosis of candidemia in patients with sepsis remains poor.

Objective

We aimed to use machine learning algorithms to develop and validate a candidemia prediction model for patients with cancer.

Methods

We conducted a single-center retrospective study using the cancer registry of a tertiary academic hospital. Adult patients diagnosed with malignancies between January 2010 and December 2018 were included. Our study outcome was the prediction of candidemia events. A stratified undersampling method was used to extract control data for algorithm learning. Multiple models were developed-a combination of 4 variable groups and 5 algorithms (auto-machine learni

SUBMITTER: Yoo J 

PROVIDER: S-EPMC8367162 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature