{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T23:57:24Z","timestamp":1793318244993,"version":"4.5.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R33HD105593"],"award-info":[{"award-number":["R33HD105593"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003141","name":"National Council of Science and Technology of Mexico","doi-asserted-by":"publisher","award":["739528"],"award-info":[{"award-number":["739528"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>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).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/infdis\/jiaf004","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:02:29Z","timestamp":1736208149000},"page":"931-939","source":"Crossref","is-referenced-by-count":1,"title":["Distinguishing Multisystem Inflammatory Syndrome in Children From Typhus Using Artificial Intelligence: MIS-C Versus Endemic Typhus (AI-MET)"],"prefix":"10.1093","volume":"231","author":[{"given":"Angela","family":"Chun","sequence":"first","affiliation":[{"name":"Division of Rheumatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital , Houston, Texas ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7661-3522","authenticated-orcid":false,"given":"Abraham","family":"Bautista-Castillo","sequence":"additional","affiliation":[{"name":"Computational Biomedicine Laboratory, Department of Computer Science, University of Houston , Houston, Texas ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9431-0829","authenticated-orcid":false,"given":"Isabella","family":"Osuna","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital , Houston, Texas ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristiana","family":"Nasto","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine , Houston, Texas ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0457-7689","authenticated-orcid":false,"given":"Flor M","family":"Munoz","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital , Houston, Texas ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gordon E","family":"Schutze","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital , Houston, Texas ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sridevi","family":"Devaraj","sequence":"additional","affiliation":[{"name":"Department of Pathology and Immunology, Baylor College of Medicine and Texas Children's Hospital , Houston, Texas 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