{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["4(10)"],"submitter":["Lam JY"],"pubmed_abstract":["Background
Multisystem inflammatory syndrome in children (MIS-C) is a novel disease that was identified during the COVID-19 pandemic and is characterised by systemic inflammation following SARS-CoV-2 infection. Early detection of MIS-C is a challenge given its clinical similarities to Kawasaki disease and other acute febrile childhood illnesses. We aimed to develop and validate an artificial intelligence algorithm that can distinguish among MIS-C, Kawasaki disease, and other similar febrile illnesses and aid in the diagnosis of patients in the emergency department and acute care setting.Methods
In this retrospective model development and validation study, we developed a deep-learning algorithm called KIDMATCH (Kawasaki Disease vs Multisystem Inflammatory Syndrome in Childre"],"journal":["The Lancet. Digital health"],"pagination":["e717-e726"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9507344"],"repository":["biostudies-literature"],"pubmed_title":["A machine-learning algorithm for diagnosis of multisystem inflammatory syndrome in children and Kawasaki disease in the USA: a retrospective model development and validation study."],"pmcid":["PMC9507344"],"pubmed_authors":["Cohen HJ","Manaloor JJ","Scalici P","Kumar M","Rosenkranz M","Hao S","D'Addese L","Gutglass DJ","Bainto E","Vayngortin T","Gardiner MA","Hite M","Nguyen MB","Rometo A","Hogan AH","Melish M","Abe N","Sykes M","Dionne A","Donofrio-Odmann JJ","Sivilay N","Salazar JC","DeBiasi RL","Szmuszkovicz JR","Rowley AH","Mohandas S","Bryl AW","Lam JY","Tremoulet AH","Ekpenyong A","Shimizu C","Bocchini J","CHARMS Study Group","Ang JY","Burns JC","Harahsheh AS","Roberts SC","Kanegaye JT","Ulrich S","Natale JE","Schwartz K","Pediatric Emergency Medicine Kawasaki Disease Research Group","Austin-Page LR","Nemati S","Jone PN","Anderson M","Samuy N","Zimmerman E","Ling XB","Mahanta S","Ansusinha E","Dominguez S","Gutierrez MP","Ashouri N","Newburger JW","Morgan L"],"additional_accession":[]},"is_claimable":false,"name":"A machine-learning algorithm for diagnosis of multisystem inflammatory syndrome in children and Kawasaki disease in the USA: a retrospective model development and validation study.","description":"Background
Multisystem inflammatory syndrome in children (MIS-C) is a novel disease that was identified during the COVID-19 pandemic and is characterised by systemic inflammation following SARS-CoV-2 infection. Early detection of MIS-C is a challenge given its clinical similarities to Kawasaki disease and other acute febrile childhood illnesses. We aimed to develop and validate an artificial intelligence algorithm that can distinguish among MIS-C, Kawasaki disease, and other similar febrile illnesses and aid in the diagnosis of patients in the emergency department and acute care setting.Methods
In this retrospective model development and validation study, we developed a deep-learning algorithm called KIDMATCH (Kawasaki Disease vs Multisystem Inflammatory Syndrome in Childre","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Oct","modification":"2025-04-21T20:06:39.955Z","creation":"2024-12-03T23:42:50.875Z"},"accession":"S-EPMC9507344","cross_references":{"pubmed":["36150781"],"doi":["10.1016/S2589-7500(22)00149-2"]}}