Data mining approaches for the detection of signals associated with pediatric post-acute sequelae of COVID-19 medrxivpreprint UW OhioState harvardmed longcovid COVID19 coronvirus covid
By Bhavana KunkalikarDec 16 2022Reviewed by Danielle Ellis, B.Sc. In a recent study posted to the medRxiv* preprint server, researchers described tree-based scan statistics for pediatric long coronavirus disease 2019 .
About the study In the present study, researchers aimed to discover PASC signals using data mining instead of clinical experience. During the study's observation period, virus testing was still commonly performed in healthcare settings. Patients were deemed SARS-CoV-2 uninfected if all diagnostic tests such as an antigen, PCR, and serology were negative during the study period, and the patient had no diagnosis codes that indicated COVID-19, multisystem inflammatory syndrome in children , or PASC.
The team used utilized the International Classification of Diseases 10th Revision vocabulary as inputs. The hierarchy followed had seven levels of nodes corresponding to each level of the tree scan. The hierarchy was referred to alternately as a tree while the cluster that contained a node a along with its descendants were termed as the branch of a tree, or a cut.
Within the branch describing uncategorized signs and symptoms, the three leading cuts corresponded to respiratory and circulatory symptoms, general symptoms, and cognitive, perceptual, emotional, and behavioral symptoms.
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