Open AccessData‐driven discovery of a clinical route for severity detection of COVID‐19 paediatric cases
Author(s) -
Yu Hui,
Guo Yuqi,
Xiang Yun,
Sun Chuan,
Yang Tao
Publication year - 2020
Publication title -
iet cyber‐systems and robotics
Language(s) - English
Resource type - Journals
ISSN - 2631-6315
DOI - 10.1049/iet-csr.2020.0037
Subject(s) - covid-19 , medicine , decision tree , retrospective cohort study , pediatrics , random forest , emergency medicine , data mining , computer science , artificial intelligence , disease , pathology , infectious disease (medical specialty) , outbreak
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