Distinguishing Multisystem Inflammatory Syndrome in Children From Typhus Using Artificial Intelligence: MIS-C Versus Endemic Typhus (AI-MET).

ABSTRACT:

Background

The pandemic emergent disease multisystem inflammatory syndrome in children (MIS-C) following coronavirus disease-19 infection can mimic endemic typhus. We aimed to use artificial intelligence (AI) to develop a clinical decision support system that accurately distinguishes MIS-C versus endemic typhus (MET).

Methods

Demographic, clinical, and laboratory features rapidly available following presentation were extracted for 133 patients with MIS-C and 87 patients hospitalized due to typhus. An attention module assigned importance to inputs used to create the 2-phase AI-MET. Phase 1 uses 17 features to arrive at a classification manually (MET-17). If the confidence level is not surpassed, 13 additional features are added to calculate MET-30 using a recurrent neural net

SUBMITTER: Chun A 

PROVIDER: S-EPMC11998560 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature