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Multisystem inflammatory syndrome in children (MIS-C) is a novel disease identified during the COVID-19 pandemic characterized by systemic inflammation following SARS-CoV-2 infection. Delays in diagnosing MIS-C may lead to more severe disease with cardiac dysfunction or death. Most pediatric patients recover fully with anti-inflammatory treatments, but early detection of MIS-C remains a challenge given its clinical similarities to Kawasaki disease (KD) and other acute childhood illnesses. We present KIDMATCH (KawasakI Disease vs Multisystem InflAmmaTory syndrome in CHildren), a deep learning algorithm for screening patients for MIS-C, KD, or other febrile illness. KIDMATCH incorporates a conformal prediction framework to tag patients with erroneous data or distribution shifts, enhancing the model generalizability and confidence by flagging unfamiliar cases as indeterminate instead of making spurious predictions. Using routinely collected clinical and laboratory data, KIDMATCH achieved a high area under the curve [0.95-0.98]. Additional external validation of this model on MIS-C patients from 16 hospitals across the United States achieved a positive detection rate of over 90%. Our algorithm has the potential to aid frontline clinicians with distinguishing between MIS-C, KD, and similar febrile illnesses in a timely manner to allow prompt treatment and prevent severe complications.

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