About: Enhanced COVID-19 data for improved prediction of survival

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Enhanced COVID-19 data for improved prediction of survival
Creator	Gautam, Anupam Huson, Daniel Zeng, Wenhuan
source	BioRxiv
abstract	The current COVID-19 pandemic, caused by the rapid world-wide spread of the SARS-CoV-2 virus, is having severe consequences for human health and the world economy. The virus effects individuals quite differently, with many infected patients showing only mild symptoms, and others showing critical illness. To lessen the impact of the pandemic, one important question is which factors predict the death of a patient? Here, we construct an enhanced COVID-19 dataset by processing two existing databases (from Kaggle and WHO) and using natural language processing methods to enhance the data by adding local weather conditions and research sentiment. Author summary In this study, we contribute an enhanced COVID-19 dataset, which contains 183 samples and 43 features. Application of Extreme Gradient Boosting (XGBoost) on the enhanced dataset achieves 95% accuracy in predicting patients survival, with country-wise research sentiment, and then age and local weather, showing the most importance. All data and source code are available at http://ab.inf.uni-tuebingen.de/publications/papers/COVID-19.
has issue date	2020-07-08(xsd:dateTime)
bibo:doi	10.1101/2020.07.08.193144
has license	biorxiv
sha1sum (hex)	a7914094ecc6e1748a2f3f90c9da61a2ac5d798c
schema:url	https://doi.org/10.1101/2020.07.08.193144
resource representing a document's title	Enhanced COVID-19 data for improved prediction of survival
schema:publication	bioRxiv
resource representing a document's body	covid:a7914094ecc6e1748a2f3f90c9da61a2ac5d798c#body_text
is schema:about of	named entity 'symptoms' named entity 'infected' named entity 'data' named entity 'SARS-CoV-2' named entity 'economy' »more»

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