Automatic diagnosis of COVID-19 infection based on ontology reasoning

Abstract Background 2019-nCoV has been spreading around the world and becoming a global concern. To prevent further widespread of 2019-nCoV, confirmed and suspected cases of COVID-19 infection are suggested to be kept in quarantine. However, the diagnose of COVID-19 infection is quite time-consuming...

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Autores principales:	Huanhuan Wu, Yichen Zhong, Yingjie Tian, Shan Jiang, Lingyun Luo
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	COVID-19 Ontology Diagnostic rules SWRL rules Automated diagnosis Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/60b976cc1d79459b88994c05900dbd10
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spelling	oai:doaj.org-article:60b976cc1d79459b88994c05900dbd102021-11-21T12:28:57ZAutomatic diagnosis of COVID-19 infection based on ontology reasoning10.1186/s12911-021-01629-01472-6947https://doaj.org/article/60b976cc1d79459b88994c05900dbd102021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01629-0https://doaj.org/toc/1472-6947Abstract Background 2019-nCoV has been spreading around the world and becoming a global concern. To prevent further widespread of 2019-nCoV, confirmed and suspected cases of COVID-19 infection are suggested to be kept in quarantine. However, the diagnose of COVID-19 infection is quite time-consuming and labor-intensive. To alleviate the burden on the medical staff, we have done some research on the intelligent diagnosis of COVID-19. Methods In this paper, we constructed a COVID-19 Diagnosis Ontology (CDO) by utilizing Protégé, which includes the basic knowledge graph of COVID-19 as well as diagnostic rules translated from Chinese government documents. Besides, SWRL rules were added into the ontology to infer intimate relationships between people, thus facilitating the efficient diagnosis of the suspected cases of COVID-19 infection. We downloaded real-case data and extracted patients’ syndromes from the descriptive text, so as to verify the accuracy of this experiment. Results After importing those real instances into Protégé, we demonstrated that the COVID-19 Diagnosis Ontology showed good performances to diagnose cases of COVID-19 infection automatically. Conclusions In conclusion, the COVID-19 Diagnosis Ontology will not only significantly reduce the manual input in the diagnosis process of COVID-19, but also uncover hidden cases and help prevent the widespread of this epidemic.Huanhuan WuYichen ZhongYingjie TianShan JiangLingyun LuoBMCarticleCOVID-19OntologyDiagnostic rulesSWRL rulesAutomated diagnosisComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss S9, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 Ontology Diagnostic rules SWRL rules Automated diagnosis Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	COVID-19 Ontology Diagnostic rules SWRL rules Automated diagnosis Computer applications to medicine. Medical informatics R858-859.7 Huanhuan Wu Yichen Zhong Yingjie Tian Shan Jiang Lingyun Luo Automatic diagnosis of COVID-19 infection based on ontology reasoning
description	Abstract Background 2019-nCoV has been spreading around the world and becoming a global concern. To prevent further widespread of 2019-nCoV, confirmed and suspected cases of COVID-19 infection are suggested to be kept in quarantine. However, the diagnose of COVID-19 infection is quite time-consuming and labor-intensive. To alleviate the burden on the medical staff, we have done some research on the intelligent diagnosis of COVID-19. Methods In this paper, we constructed a COVID-19 Diagnosis Ontology (CDO) by utilizing Protégé, which includes the basic knowledge graph of COVID-19 as well as diagnostic rules translated from Chinese government documents. Besides, SWRL rules were added into the ontology to infer intimate relationships between people, thus facilitating the efficient diagnosis of the suspected cases of COVID-19 infection. We downloaded real-case data and extracted patients’ syndromes from the descriptive text, so as to verify the accuracy of this experiment. Results After importing those real instances into Protégé, we demonstrated that the COVID-19 Diagnosis Ontology showed good performances to diagnose cases of COVID-19 infection automatically. Conclusions In conclusion, the COVID-19 Diagnosis Ontology will not only significantly reduce the manual input in the diagnosis process of COVID-19, but also uncover hidden cases and help prevent the widespread of this epidemic.
format	article
author	Huanhuan Wu Yichen Zhong Yingjie Tian Shan Jiang Lingyun Luo
author_facet	Huanhuan Wu Yichen Zhong Yingjie Tian Shan Jiang Lingyun Luo
author_sort	Huanhuan Wu
title	Automatic diagnosis of COVID-19 infection based on ontology reasoning
title_short	Automatic diagnosis of COVID-19 infection based on ontology reasoning
title_full	Automatic diagnosis of COVID-19 infection based on ontology reasoning
title_fullStr	Automatic diagnosis of COVID-19 infection based on ontology reasoning
title_full_unstemmed	Automatic diagnosis of COVID-19 infection based on ontology reasoning
title_sort	automatic diagnosis of covid-19 infection based on ontology reasoning
publisher	BMC
publishDate	2021
url	https://doaj.org/article/60b976cc1d79459b88994c05900dbd10
work_keys_str_mv	AT huanhuanwu automaticdiagnosisofcovid19infectionbasedonontologyreasoning AT yichenzhong automaticdiagnosisofcovid19infectionbasedonontologyreasoning AT yingjietian automaticdiagnosisofcovid19infectionbasedonontologyreasoning AT shanjiang automaticdiagnosisofcovid19infectionbasedonontologyreasoning AT lingyunluo automaticdiagnosisofcovid19infectionbasedonontologyreasoning
_version_	1718419009623293952

Automatic diagnosis of COVID-19 infection based on ontology reasoning

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