Constructing, validating, and updating machine learning models to predict survival in children with Ebola Virus Disease.

ABSTRACT:

Background

Ebola Virus Disease (EVD) causes high case fatality rates (CFRs) in young children, yet there are limited data focusing on predicting mortality in pediatric patients. Here we present machine learning-derived prognostic models to predict clinical outcomes in children infected with Ebola virus.

Methods

Using retrospective data from the Ebola Data Platform, we investigated children with EVD from the West African EVD outbreak in 2014-2016. Elastic net regularization was used to create a prognostic model for EVD mortality. In addition to external validation with data from the 2018-2020 EVD epidemic in the Democratic Republic of the Congo (DRC), we updated the model using selected serum biomarkers.

Findings

Pediatric EVD mortality was significantly associated wit

SUBMITTER: Genisca AE 

PROVIDER: S-EPMC9555640 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature