학술논문
Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results with Routine Blood Tests
Document Type
Academic Journal
Source
Laboratory Medicine. March 2021, Vol. 52 Issue 2, p146, 4 p.
Subject
Language
English
ISSN
0007-5027
Abstract
The diagnosis of COVID-19 is based on the detection of SARS-CoV-2 in respiratory secretions, blood, or stool. (1,2) Currently, reverse-transcription polymerase chain reaction (RT-PCR) is the most commonly used method [...]
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. Abbreviations: RT-PCR, reverse-transcription polymerase chain reaction; ROC, receiver operating characteristic; WHO, World Health Organization.
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. Abbreviations: RT-PCR, reverse-transcription polymerase chain reaction; ROC, receiver operating characteristic; WHO, World Health Organization.