About: Machine Learning Approaches in COVID-19 Survival Analysis and Discharge Time Likelihood Prediction using Clinical Data

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.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 Approaches in COVID-19 Survival Analysis and Discharge Time Likelihood Prediction using Clinical Data
Creator	Ansary, Jamal Nemati, Mohammadreza Nemati, Nazafarin
Source	Elsevier; Medline; PMC
abstract	Summary As a highly contagious respiratory disease, COVID-19 has yielded high mortality rates since its emergence in December of 2019. As the number of COVID-19 cases soars in epicenters, health officials are warning about the possibility of the designated treatment centers being overwhelmed by coronavirus patients. In this study, several computational techniques are implemented to analyze the survival characteristics of 1182 patients. The computational results agree with the outcome reported in early clinical reports released for a group of patients from China that confirmed a higher mortality rate in men compared to women and in older age groups. The discharge time prediction of COVID-19 patients was also evaluated using different machine learning and statistical analysis methods. The results indicate that the Gradient Boosting survival model outperforms other models for patient survival prediction in this study. This research study is aimed to help health officials make more educated decisions during the outbreak.
has issue date	2020-07-04(xsd:dateTime)
bibo:doi	10.1016/j.patter.2020.100074
bibo:pmid	32835314
has license	els-covid
sha1sum (hex)	b5da363165b401a62785a7c9eb8baee919f12c75
schema:url	https://doi.org/10.1016/j.patter.2020.100074
resource representing a document's title	Machine Learning Approaches in COVID-19 Survival Analysis and Discharge Time Likelihood Prediction using Clinical Data
has PubMed Central identifier	PMC7334917
has PubMed identifier	32835314
schema:publication	Patterns (N Y)
resource representing a document's body	covid:b5da363165b401a62785a7c9eb8baee919f12c75#body_text
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