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.
【초록키워드】 SARS-CoV-2, SARS-COV-2 infection, machine learning, RT-PCR, Laboratory, reverse transcription polymerase chain reaction, polymerase chain reaction, Stool, Accuracy, SARS-CoV-2 testing, Algorithm, Patient, reverse transcription, PCR test, characteristic, patients, predict, Blood, RT-PCR test, Analysis, test result, receiver operating characteristic, Chain Reaction, blood tests, receiver, machine, SARS-CoV-2 RT-PCR test, retrospective cohort, SARS-CoV-2 test, forest, routine blood tests, feature, polymerase chain, performed, evaluated, unique, diagnosis of COVID-19, routine blood test, 【제목키워드】 SARS-CoV-2, Test, prediction, reaction, chain, routine, Result,