Data‐driven discovery of a clinical route for severity detection of COVID‐19 paediatric cases | Zendy

Yu Hui | Zendy; Guo Yuqi | Zendy; Xiang Yun | Zendy; Sun Chuan | Zendy; Yang Tao | Zendy

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Data‐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

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