[2Xin5-04] Machine Learning to Predict COVID-19 PCR Positivity Using Blood Test Data
Keywords:COVID-19, blood test, Prediction of PCR positivity
During the Spring in 2020 coronavirus epidemic (COVID-19), PCR testing resources were scarce, and there was a need to tighten testing standards.
The blood tests of COVID-19 PCR-positive patients were known to show certain trends in white blood cell count (WBC), C-reactive protein (CRP), lymphocyte count (Lymph), and platelet count (PLT), but no cutoff values were found.
In order to determine the cut-off values, statistical analysis and supervised machine learning (ensemble learning of neural networks and gradient boosting trees) were performed on 328 patients who underwent PCR and blood tests simultaneously. The supervised machine learning with 27 explanatory variables, which were significantly different by statistical testing, showed an AUC of 83.6% (sensitivity 63.2%, specificity 94.1%). Factors that contributed highly to prediction were (1) presence of a co-resident with similar symptoms, (2) presence of cough, (3) PLT, and (4) presence of taste abnormalities. The cut-off values of WBC less than 5,200, Lymph less than 1,000, PLT less than 200,000, and CRP less than 10 were good discriminators in blood tests.
By adding the blood data points with the above cut-off values to the environmental factors and clinical symptoms, we were able to provide physicians with supplementary information for making decisions on PCR testing.
The blood tests of COVID-19 PCR-positive patients were known to show certain trends in white blood cell count (WBC), C-reactive protein (CRP), lymphocyte count (Lymph), and platelet count (PLT), but no cutoff values were found.
In order to determine the cut-off values, statistical analysis and supervised machine learning (ensemble learning of neural networks and gradient boosting trees) were performed on 328 patients who underwent PCR and blood tests simultaneously. The supervised machine learning with 27 explanatory variables, which were significantly different by statistical testing, showed an AUC of 83.6% (sensitivity 63.2%, specificity 94.1%). Factors that contributed highly to prediction were (1) presence of a co-resident with similar symptoms, (2) presence of cough, (3) PLT, and (4) presence of taste abnormalities. The cut-off values of WBC less than 5,200, Lymph less than 1,000, PLT less than 200,000, and CRP less than 10 were good discriminators in blood tests.
By adding the blood data points with the above cut-off values to the environmental factors and clinical symptoms, we were able to provide physicians with supplementary information for making decisions on PCR testing.
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