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

Authors

• Angela Chun
• Abraham Bautista-Castillo
• Isabella Osuna
• Kristiana Nasto
• Flor M Munoz
• Gordon E Schutze
• Sridevi Devaraj
• Eyal Muscal
• Marietta M de Guzman
• Kristen Sexson Tejtel
• Tiphanie P Vogel
• Ioannis A Kakadiaris

Language

English

Publication Date

4-15-2025

Journal

The Journal of Infectious Diseases

DOI

10.1093/infdis/jiaf004

PMID

39761811

PMCID

PMC11998560

PubMedCentral® Posted Date

1-7-2025

PubMedCentral® Full Text Version

Post-print

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 network.

Results: While 24 of 30 features differed statistically, the values overlapped sufficiently that the features were clinically irrelevant distinguishers as individual parameters. However, AI-MET successfully classified typhus and MIS-C with 100% accuracy. A validation cohort of 111 additional patients with MIS-C was classified with 99% accuracy.

Conclusions: Artificial intelligence can successfully distinguish MIS-C from typhus using rapidly available features. This decision support system will be a valuable tool for front-line providers facing the difficulty of diagnosing a febrile child in endemic areas.

Keywords

Humans, Child, Artificial Intelligence, COVID-19, Male, Systemic Inflammatory Response Syndrome, Female, Child, Preschool, Diagnosis, Differential, Typhus, Endemic Flea-Borne, Adolescent, Scrub Typhus, SARS-CoV-2, Infant, multisystem inflammatory syndrome in children (MIS-C), endemic typhus, murine typhus, machine learning, artificial intelligence

Published Open-Access

yes

Recommended Citation

Chun, Angela; Bautista-Castillo, Abraham; Osuna, Isabella; et al., "Distinguishing Multisystem Inflammatory Syndrome in Children From Typhus Using Artificial Intelligence: MIS-C Versus Endemic Typhus (AI-MET)" (2025). Faculty, Staff and Students Publications. 7460.
https://digitalcommons.library.tmc.edu/baylor_docs/7460