About: Machine learning based approaches for detecting COVID-19 using clinical text data

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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 approaches for detecting COVID-19 using clinical text data
Creator	Din, Ud Syed, • Rouf, Nusrat Din Khanday, Ud Khan, Rayees Mohi, Akib Mohi, Masarat Qamar, • Rabani, Tanzeel
Source	Medline; PMC
abstract	Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.
has issue date	2020-06-30(xsd:dateTime)
bibo:doi	10.1007/s41870-020-00495-9
bibo:pmid	32838125
has license	no-cc
sha1sum (hex)	1357b1488611c5474e8fce33bee2319f27759a62
schema:url	https://doi.org/10.1007/s41870-020-00495-9
resource representing a document's title	Machine learning based approaches for detecting COVID-19 using clinical text data
has PubMed Central identifier	PMC7325639
has PubMed identifier	32838125
schema:publication	Int J Inf Technol
resource representing a document's body	covid:1357b1488611c5474e8fce33bee2319f27759a62#body_text
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