About: Machine learning based clinical decision supportsystem for early COVID-19 mortality prediction

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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	Machine learning based clinical decision supportsystem for early COVID-19 mortality prediction
Creator	Deva Priyakumar, U Garg, Akshit Karthikeyan, Akshaya Vinod, P
Source	MedRxiv
abstract	The coronavirus disease 2019 (COVID-19) is an acute respiratory disease that has been classified as a pandemic by World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public medical systems. Hence, it is crucial to identify the key factors of mortality that yield high accuracy and consistency to optimize patient treatment strategy. This study uses machine learning methods to identify a powerful combination of five features that help predict mortality with 96% accuracy: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP) and age. Various machine learning algorithms have been compared to achieve a consistent high accuracy across the days that span the disease. Robust testing with three cases confirm the strong predictive performance of the proposed model. The model predicts with an accuracy of 90% as early as 16 days before the outcome. This study would help accelerate the decision making process in healthcare systems for focused medical treatments early and accurately.
has issue date	2020-08-22(xsd:dateTime)
bibo:doi	10.1101/2020.08.19.20177477
has license	medrxiv
sha1sum (hex)	48fb16f0b3d9d0d87c048d702711b76380bd9e1b
schema:url	https://doi.org/10.1101/2020.08.19.20177477
resource representing a document's title	Machine learning based clinical decision supportsystem for early COVID-19 mortality prediction
resource representing a document's body	covid:48fb16f0b3d9d0d87c048d702711b76380bd9e1b#body_text
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