Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam.

ABSTRACT:

Background

Identifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context.

Methods

We developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fo

SUBMITTER: Ming DK 

PROVIDER: S-EPMC9931311 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature