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

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study Goto Sponge NotDistinct Permalink

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	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 Locatelli, Massimo Federico Cabitza, ·
Source	Medline; PMC
abstract	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. 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. 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. 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	2020-07-01(xsd:dateTime)
bibo:doi	10.1007/s10916-020-01597-4
bibo:pmid	32607737
has license	no-cc
sha1sum (hex)	0ce4d432d68895071cc8ab2cd0f2fd07b8f280a3
schema:url	https://doi.org/10.1007/s10916-020-01597-4
resource representing a document's title	Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study
has PubMed Central identifier	PMC7326624
has PubMed identifier	32607737
schema:publication	J Med Syst
resource representing a document's body	covid:0ce4d432d68895071cc8ab2cd0f2fd07b8f280a3#body_text
is schema:about of	named entity 'Thus' named entity 'cases' named entity 'higher' named entity 'presents' named entity 'interpreting' named entity 'test' named entity 'alternative' named entity 'patients' named entity 'infected' named entity 'COVID-19' named entity 'aid' named entity 'respiratory tract' named entity 'Blood' named entity 'LDH' named entity 'TIMES' named entity 'TRAINED' named entity 'INFECTION' named entity 'DEVELOPING' named entity 'DEATHS' named entity 'CONFIRMATION OF' named entity 'REVERSE TRANSCRIPTION POLYMERASE CHAIN REACTION' named entity 'ANALYSIS' named entity 'real time' named entity 'suffering' named entity 'polymerase' named entity 'symptoms' named entity 'tool' named entity 'sensitivity' named entity 'screened' named entity 'demonstrated' named entity 'potential' named entity 'white blood cells' named entity 'false-negative' named entity 'expensive' named entity 'trained' named entity 'This' named entity 'reagents' named entity 'Tree model' named entity 'SIGNAL' named entity 'plasma levels' named entity 'COVID' named entity 'gold standard' named entity 'San Raffaele Hospital' named entity 'Milan' named entity 'infection' named entity 'reverse transcription polymerase chain reaction' named entity 'blood tests' named entity 'viral RNA' named entity 'Italy' named entity 'gold standard test' named entity 'GGT' named entity 'machine learning' named entity 'SARS-CoV-2' named entity 'COVID-19' named entity 'SIGNAL PROCESSING' named entity 'Decision Tree' named entity 'decreased lymphocyte count' named entity 'Decision Tree model' named entity 'infection' named entity 'COVID' named entity 'COVID' named entity 'GDPR' named entity 'infection' named entity 'low level of white blood cells' named entity 'Random Forest' named entity 'algorithm' named entity 'Naïve Bayes' named entity 'biochemist' named entity 'Logistic Regression' named entity 'WBC' named entity 'algorithm' named entity 'Decision Tree' named entity 'ground truth' named entity 'COVID-19 infection' named entity 'imputation method'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software