About: Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: a Feasibility Study

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: a Feasibility Study
Creator	Banfi, Giuseppe Brinati, Davide Cabitza, Federico Campagner, Andrea Ferrari, Davide »more»
source	MedRxiv
abstract	Background - The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests. Material and methods - We developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergency-room with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response. Results - We have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases. Discussion - This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation. This tool is available at https://covid19-blood-ml.herokuapp.com.
has issue date	2020-04-25(xsd:dateTime)
bibo:doi	10.1101/2020.04.22.20075143
has license	medrxiv
sha1sum (hex)	05ceee276cfbb202ae4177f047f30d255d9aba9e
schema:url	https://doi.org/10.1101/2020.04.22.20075143
resource representing a document's title	Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: a Feasibility Study
resource representing a document's body	covid:05ceee276cfbb202ae4177f047f30d255d9aba9e#body_text
is schema:about of	named entity 'patients' named entity 'worldwide' named entity 'expensive' named entity 'interpretable' named entity 'values' »more»

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