Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.

ABSTRACT:

Background

Asthma is associated with an increased risk of acute respiratory infections (ARI). Little is known about whether natural language processing (NLP)-powered digital biomarkers can identify a high-risk asthma subgroup for ARI during early childhood.

Objective

We assessed whether a digital biomarker could identify a high-risk subgroup of childhood asthma for ARI.

Methods

We applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) and Asthma Predictive Index (NLP-API) to electronic health records of the 1997-2016 Mayo Clinic Birth Cohort. We categorized the cohort into 4 subgroups: both criteria positive (NLP^-PAC⁺/NLP^-API⁺), PAC positive only (NLP^-PAC⁺), API positive only (NLP<

SUBMITTER: Juhn YJ 

PROVIDER: S-EPMC12513160 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature