{"doi":"10.1093/infdis/jiaf004","title":"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 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.","journal":"The Journal of Infectious Diseases","year":2025,"id":559992,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9476,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1176222,"name":"Abraham Bautista-Castillo","orcid":"0000-0001-7661-3522","position":1,"is_corresponding":false},{"id":927467,"name":"Isabella Osuna","orcid":"0009-0002-9431-0829","position":2,"is_corresponding":false},{"id":1462173,"name":"Kristiana Nasto","orcid":null,"position":3,"is_corresponding":false},{"id":236975,"name":"Flor M. Muñoz","orcid":"0000-0002-0457-7689","position":4,"is_corresponding":false},{"id":598936,"name":"Gordon E. Schutze","orcid":null,"position":5,"is_corresponding":false},{"id":255990,"name":"Sridevi Devaraj","orcid":"0000-0001-9189-7914","position":6,"is_corresponding":false},{"id":939080,"name":"Eyal Muscal","orcid":"0000-0003-1866-6129","position":7,"is_corresponding":false},{"id":1040434,"name":"Marietta M. de Guzman","orcid":"0009-0006-5751-5162","position":8,"is_corresponding":false},{"id":250497,"name":"S. Kristen Sexson Tejtel","orcid":"0000-0001-7584-8450","position":9,"is_corresponding":false},{"id":254732,"name":"Tiphanie P. Vogel","orcid":"0000-0002-0857-133X","position":10,"is_corresponding":false},{"id":527046,"name":"Ioannis A. Kakadiaris","orcid":"0000-0002-0591-1079","position":11,"is_corresponding":false},{"id":939079,"name":"Angela Chun","orcid":"0000-0002-2768-5383","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":null,"created_at":"2026-07-19T02:55:39.010633Z","pmid":"39761811","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}