About: Ontology-based annotation and analysis of COVID-19 phenotypes

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Ontology-based annotation and analysis of COVID-19 phenotypes
Creator	Wang, Yang He, Yongqun Yu, Hong Zhang, Fengwei Ye, Xianwei
Source	ArXiv
abstract	The epidemic of COVID-19 has caused an unpredictable and devastated disaster to the public health in different territories around the world. Common phenotypes include fever, cough, shortness of breath, and chills. With more cases investigated, other clinical phenotypes are gradually recognized, for example, loss of smell, and loss of tastes. Compared with discharged or cured patients, severe or died patients often have one or more comorbidities, such as hypertension, diabetes, and cardiovascular disease. In this study, we systematically collected and analyzed COVID-19-related clinical phenotypes from 70 articles. The commonly occurring 17 phenotypes were classified into different groups based on the Human Phenotype Ontology (HPO). Based on the HP classification, we systematically analyze three nervous phenotypes (loss of smell, loss of taste, and headache) and four abdominal phenotypes (nausea, vomiting, abdominal pain, and diarrhea) identified in patients, and found that patients from Europe and USA turned to have higher nervous phenotypes and abdominal phenotypes than patients from Asia. A total of 23 comorbidities were found to commonly exist among COVID-19 patients. Patients with these comorbidities such as diabetes and kidney failure had worse outcomes compared with those without these comorbidities.
has issue date	2020-08-05(xsd:dateTime)
has license	arxiv
sha1sum (hex)	c1c250c4b053193eb610ecf42a9f61b0934ed656
resource representing a document's title	Ontology-based annotation and analysis of COVID-19 phenotypes
resource representing a document's body	covid:c1c250c4b053193eb610ecf42a9f61b0934ed656#body_text
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