Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study.
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 (rea...
| Published in: | Journal of Medical Systems Vol. 44; no. 8; pp. 1 - 13 |
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| Main Authors: | , , , , , |
| Format: | algorithm research tables/charts Journal Article |
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Springer Nature
Aug2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144920605&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144920605 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2020 vid: 44 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 144920605 144920605 144920605 10.1007/s10916-020-01597-4 144920605 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. aug: au: Brinati, Davide Campagner, Andrea Ferrari, Davide Locatelli, Massimo Banfi, Giuseppe Cabitza, Federico affil: DISCo, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126, Milano, Italy sug: subj: COVID-19 Diagnosis Diagnostic Tests, Routine Machine Learning Human Italy Pilot Studies Reverse Transcriptase Polymerase Chain Reaction Female Male Adult Middle Age Aged Descriptive Statistics Decision Trees Random Forest Leukocyte Count Platelet Count C-Reactive Protein gamma-Glutamyltransferase Lactate Dehydrogenase Hematologic Tests Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: 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/). pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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