| Autor: |
Lam, Jonathan Y, Shimizu, Chisato, Tremoulet, Adriana H, Bainto, Emelia, Roberts, Samantha C, Sivilay, Nipha, Gardiner, Michael A, Kanegaye, John T, Hogan, Alexander H, Salazar, Juan C, Mohandas, Sindhu, Szmuszkovicz, Jacqueline R, Mahanta, Simran, Dionne, Audrey, Newburger, Jane W, Ansusinha, Emily, DeBiasi, Roberta L, Hao, Shiying, Ling, Xuefeng B, Cohen, Harvey J, Nemati, Shamim, Burns, Jane C, Abe, Naomi, Austin-Page, Lukas R., Bryl, Amy W., Donofrio-Odmann, J Joelle, Ekpenyong, Atim, Gutglass, David J., Nguyen, Margaret B., Schwartz, Kristy, Ulrich, Stacey, Vayngortin, Tatyana, Zimmerman, Elise, Anderson, Marsha, Ang, Jocelyn Y., Ashouri, Negar, Bocchini, Joseph, D'Addese, Laura, Dominguez, Samuel, Gutierrez, Maria Pila, Harahsheh, Ashraf S., Hite, Michelle, Jone, Pei-Ni, Kumar, Madan, Manaloor, John J., Melish, Marian, Morgan, Lerraughn, Natale, JoAnne E., Rometo, Allison, Rosenkranz, Margalit, Rowley, Anne H., Samuy, Nichole, Scalici, Paul, Sykes, Michelle |
| Zdroj: |
The Lancet Digital Health; October 2022, Vol. 4 Issue: 10 pe717-e726, 10p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
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