Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results

Abstract

Since the sudden outbreak of coronavirus disease 2019 , it has rapidly evolved into a momentous global health concern. Due to the lack of constructive information on the pathogenesis of COVID-19 and specific treatment, it highlights the importance of early diagnosis and timely treatment.In this study, 11 key blood indices were extracted through random forest algorithm to build the final assistant discrimination tool from 49 clinical available blood test data which were derived by commercial blood test equipments. The method presented robust outcome to accurately identify COVID-19 from a variety of suspected patients with similar CT information or similar symptoms, with accuracy of 0.9795 and 0.9697 for the cross-validation set and test set, respectively. The tool also demonstrated its outstanding performance on an external validation set that was completely independent of the modeling process, with sensitivity, specificity, and overall accuracy of 0.9512, 0.9697, and 0.9595, respectively. Besides, 24 samples from overseas infected patients with COVID-19 were used to make an in-depth clinical assessment with accuracy of 0.9167. After multiple verification, the reliability and repeatability of the tool has been fully evaluated, and it has the potential to develop into an emerging technology to identify COVID-19 and lower the burden of global public health. The : medRxiv preprint proposed tool is well-suited to carry out preliminary assessment of suspected patients and help them to get timely treatment and quarantine suggestion. The assistant tool is now available online at

Development of Machine Learning models to predict RT-PCR results for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.

“…In the present manuscript, we describe this population and highlight the differences between patients who actually tested positive to SARS-COV-2 and those who did not. Few attempts in applying arti cial intelligence to rapidly predict positivity/negativity to SARS-COV-2 were made since the outbreak, using mostly CT imaging and lab results, collected in Chinese population (46)(47)(48). Nevertheless, we present the rst European attempt and promising results applying arti cial intelligence to predict the results of RT-PCR for SARS-COV-2, using only basic clinical data, available in the vast majority of emergency departments all over the world.…”

“…Chest CT scan was analysed via deep learning by Li et al to differentiate SARS-COV-2 induced viral pneumonia from other lung diseases (63). Two other research groups developed machine learning models and online applications, using only lab test results (47,48).…”

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

“…On the other hand, solutions based on CT imaging, although accurate, are affected by the characteristics of this modality: CTs are costly, timeconsuming, and require specialized equipment; thus, approaches based on this imaging technique cannot reasonably be applied for screening exams. Although various clinical studies [11][12][13] have highlighted how blood test-based diagnostics might provide an effective and lowcost alternative for the early detection of COVID-19 cases, relatively few ML models have been applied to hematological parameters [14][15][16][17][18].…”

Use of Machine Learning to Rapidly Predict Positivity to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) Using Basic Clinical Data

“…In the present manuscript, we describe this population and highlight the differences between patients who actually tested positive to SARS-COV-2 and those who did not. Few attempts in applying arti cial intelligence torapidly predict positivity/negativity to SARS-COV-2 were made since the outbreak, using mostly CT imaging and lab results, collected in Chinese population (42)(43)(44). Nevertheless, we present the rst European attempt and promising results applying arti cial intelligence to rapidly predict positivity/negativity to SARS-COV-2 using only basic clinical data, available in the vast majority of emergency departments all over the world.…”

“…Chest CT scan was analysed via deep learning by Li et al to differentiate SARS-COV-2 induced viral pneumonia from other lung disease (60). Two other research groups developed from machine learning models free online applications, using only lab test results (43,44).…”