{"database":"ENA","file_versions":[],"scores":null,"additional":{"omics_type":["Genomics"],"center_name":["JOHNS HOPKINS UNIVERSITY"],"full_dataset_link":["https://www.ebi.ac.uk/ena/browser/view/PRJNA846922"],"scientific_name":["Homo sapiens"],"long_description":["The primary objective of this study was to design and validate a predictive decision support system for the identification, treatment, and management of SARS-CoV-2 associated with multisystem inflammatory syndrome in children (MIS-C). To develop this system, we adapted and retrained machine learning algorithms, which we previously trained in patients with Kawasaki Disease, a pediatric inflammatory vasculopathy with clinical overlap with MIS-C but different etiology. This study was performed in collaboration with the International Kawasaki Disease Registry (IKDR) consortium. This dataset contains the development and international validation data for all algorithms developed as part of this study."],"repository":["ENA"],"additional_accession":[]},"is_claimable":false,"name":"Rapid Acceleration of Diagnostics - Radical (RADx-rad): A Data Science Approach to Identify and Manage Multisystem Inflammatory Syndrome in Children (MIS-C) Associated With SARS-CoV-2 Infection and Kawasaki Disease in Pediatric Patients","description":"Rapid Acceleration of Diagnostics - Radical (RADx-rad): A Data Science Approach to Identify and Manage Multisystem Inflammatory Syndrome in Children (MIS-C) Associated With SARS-CoV-2 Infection and Kawasaki Disease in Pediatric Patients","dates":{"last_updated":"2025-09-24","first_public":"2023-01-27"},"accession":"PRJNA846922","cross_references":{"taxon":["9606"]}}