Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma.

ABSTRACT:

Objectives

Manual clinical scoring systems are the current standard used for acute asthma clinical care pathways. No automated system exists that assesses disease severity, time course, and treatment impact in pediatric acute severe asthma exacerbations.

Working hypothesis

machine learning applied to continuous vital sign data could provide a novel pediatric-automated asthma respiratory score (pARS) by using the manual pediatric asthma score (PAS) as the clinical care standard.

Methods

Continuous vital sign monitoring data (heart rate, respiratory rate, and pulse oximetry) were merged with the health record data including a provider-determined PAS in children between 2 and 18 years of age admitted to the pediatric intensive care unit (PICU) for status asthmaticus. A c

SUBMITTER: Messinger AI 

PROVIDER: S-EPMC6641986 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature