Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results with Routine Blood Tests.
Lab Med
; 52(2): 146-149, 2021 Mar 15.
Article
in English
| MEDLINE | ID: covidwho-990757
ABSTRACT
OBJECTIVE:
The diagnosis of COVID-19 is based on the detection of SARS-CoV-2 in respiratory secretions, blood, or stool. Currently, reverse transcription polymerase chain reaction (RT-PCR) is the most commonly used method to test for SARS-CoV-2.METHODS:
In this retrospective cohort analysis, we evaluated whether machine learning could exclude SARS-CoV-2 infection using routinely available laboratory values. A Random Forests algorithm with 28 unique features was trained to predict the RT-PCR results.RESULTS:
Out of 12,848 patients undergoing SARS-CoV-2 testing, routine blood tests were simultaneously performed in 1357 patients. The machine learning model could predict SARS-CoV-2 test results with an accuracy of 86% and an area under the receiver operating characteristic curve of 0.74.CONCLUSION:
Machine learning methods can reliably predict a negative SARS-CoV-2 RT-PCR test result using standard blood tests.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Machine Learning
/
COVID-19
Type of study:
Cohort study
/
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Lab Med
Year:
2021
Document Type:
Article
Affiliation country:
Labmed
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