Data‐driven discovery of a clinical route for severity detection of COVID‐19 paediatric cases. Issue 4 (18th November 2020)

• Record Type: Journal Article
• Authors: Yu, Hui; Guo, Yuqi; Xiang, Yun; Sun, Chuan; Yang, Tao
• Is Part Of: IET cyber-systems and robotics. Volume 2:Issue 4(2020)
• Page Start: 205
• Page End: 206
• Publication Date: 2020-11-18
• Subjects: epidemics -- diseases; data‐driven discovery -- clinical route -- severity detection -- COVID‐19 paediatric cases -- COVID‐19 epidemic -- infected pediatric cases -- children patients; Robotics -- Periodicals; Cybernetics -- Periodicals; Cybernetics; Robotics; Periodicals; 629
• DOI: 10.1049/iet-csr.2020.0037
• Languages: English
• ISSNs: 2631-6315

Abstract

Abstract : In this retrospective COVID‐19 study on 105 infected children admitted to Wuhan Children's Hospital, we have revealed two biomarkers (DBIL and ALT) to promptly screen out the severe ones from all the cases with the assistance of a proposed supervised decision‐tree classifier. This clinical route achieves a 100% F1‐score in the present investigation, which can be expected to facilitate early diagnosis and intervention for pediatric COVID‐19 case