Data-driven discovery of a clinical route for severity detection of COVID-19 paediatric cases

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

Data-driven discovery of a clinical route for severity detection of COVID-19 paediatric cases

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Author(s): Hui Yu¹ ; Yuqi Guo¹ ; Yun Xiang¹ ; Chuan Sun² ; Tao Yang³
- Affiliations: 1: Wuhan Children's Hospital, Tongji Medical School, Huazhong University of Science and Technology , Wuhan , People's Republic of China ;
  2: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology , Wuhan , People's Republic of China ;
  3: State Key Laboratory of Synthetical Automation for Process Industries , Northeastern University , No 3–11, Wenhua Road, Shenyang , People's Republic of China
Source: Volume 2, Issue 4, December 2020, p. 205 – 206
DOI: 10.1049/iet-csr.2020.0037 , Online ISSN 2631-6315

This is an open access article published by the IET and Zhejiang University Press under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Received 09/07/2020, Accepted 23/09/2020, Revised 06/09/2020, Published 19/10/2020

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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Data-driven discovery of a clinical route for severity detection of COVID-19 paediatric cases

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