Evidence map›Paper›PMID 39761811›Full record

ArticleThe Journal of infectious diseases2025

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

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 and 2 more

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Article in The Journal of infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Angela ChunDivision of Rheumatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.
Abraham Bautista-CastilloComputational Biomedicine Laboratory, Department of Computer Science, University of Houston, Houston, Texas, USA.ORCID 0000-0001-7661-3522
Isabella OsunaDivision of Rheumatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.ORCID 0009-0002-9431-0829
Kristiana NastoBaylor College of Medicine, Houston, Texas, USA.
Flor M MunozDivision of Infectious Diseases, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.ORCID 0000-0002-0457-7689
Gordon E SchutzeDivision of Infectious Diseases, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.
Sridevi DevarajDepartment of Pathology and Immunology, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.
Eyal MuscalDivision of Rheumatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.
Marietta M de GuzmanDivision of Rheumatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.
Kristen Sexson TejtelDivision of Cardiology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.ORCID 0000-0001-7584-8450
Tiphanie P VogelDivision of Rheumatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.ORCID 0000-0002-0857-133X
Ioannis A KakadiarisComputational Biomedicine Laboratory, Department of Computer Science, University of Houston, Houston, Texas, USA.

Funding

The PreVAIL-kIds Common ProtocolR33HD105593 · NICHD · BAYLOR COLLEGE OF MEDICINE · PI ALLEN, CARL E, ANNAPRAGADA, ANANTH V · 2023 to 2023
$6.3M
National Council of Science and Technology of Mexico 739528NICHD NIH HHS R33 HD105593NIH HHS R33HD105593
6 · The paper itself

Abstract

backgroundThe 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).

methodsDemographic, 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.

resultsWhile 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.

conclusionsArtificial 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.

Indexed as

Artificial IntelligenceCOVID-19Systemic Inflammatory Response SyndromeTyphus, Endemic Flea-BorneAdolescentChildChild, PreschoolDiagnosis, DifferentialFemaleHumansInfantMaleSARS-CoV-2Scrub Typhusartificial intelligenceendemic typhusmachine learningmultisystem inflammatory syndrome in children (MIS-C)murine typhus

Identifiers

PMID39761811
PMCPMC11998560

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.