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Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study

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Brinati, Davide, Campagner, Andrea, Ferrari, Davide, Locatelli, Massimo, Banfi, Giuseppe, Cabitza, Federico·2020·Journal of Medical Systems
DOI 10.1007/s10916-020-01597-4

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Abstract

The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests. We developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergency-room with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response. We have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases. This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation (This tool is available at https://covid19-blood-ml.herokuapp.com/).

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Research works printed but not linked: 32

  1. Ai T., Yang Z., Hou H., Zhan C., Chen C., Lv W., Tao Q., Sun Z., Xia L. (2020) Correlation of chest ct and rt-pcr testing in coronavirus disease 2019 (covid-19) in china: a report of 1014 cases. Radiology p 200642 · DOI · Paper page
  2. Altman N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46 (3): 175–185, 1992 · DOI · Paper page
  3. Anguita D., Ghio A., Greco N., et al.: Model selection for support vector machines: Advantages and disadvantages of the machine learning theory.. In: IJCNN-2010, 2010, pp 1–8, https://doi.org/10.1109/IJCNN.2010.5596450 · DOI
  4. Campagner A., Cabitza F., Ciucci D.: The three-way-in and three-way-out framework to treat and exploit ambiguity in data. International Journal of Approximate Reasoning 119: 292–312, 2020 · DOI
  5. Cawley G.C., Talbot N.L.: On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11 (Jul): 2079–2107, 2010
  6. Corman V.M., Landt O., Kaiser M., Molenkamp R., Meijer A., Chu D.K., Bleicker T., Brünink S, Schneider J., Schmidt M.L., at al.: Detection of 2019 novel coronavirus (2019-ncov) by real-time rt-pcr. Eurosurveillance 25 (3): 2000045, 2020 · DOI · Paper page
  7. Day M.: Covid-19: identifying and isolating asymptomatic people helped eliminate virus in italian village. BMJ 368: m1165, 2020 · DOI · Paper page
  8. Ferrari D., Lombardi G., Strollo M., Pontillo M., Motta A., Locatelli M.: Association between solar ultraviolet doses and vitamin d clinical routine data in european mid-latitude population between 2006 and 2018. Photochemical & Photobiological Sciences 18 (11): 2696–2706, 2019 · DOI
  9. Ferrari D., Motta A., Strollo M., Banfi G., Massimo L. (2020) Routine blood tests as a potential diagnostic tool for covid-19. Clinical Chemistry and Laboratory Medicine (2) https://doi.org/10.1515/cclm-2020-0398 · DOI · Paper page
  10. Ferri C., Hernández-Orallo J: Cautious classifiers. ROCAI 4: 27–36, 2004
  11. Geurts P., Ernst D., Wehenkel L.: Extremely randomized trees. Machine learning 63 (1): 3–42, 2006 · DOI · Paper page
  12. Gozes O., Frid-Adar M., Sagie N., Zhang H., Ji W., Greenspan H. (2020) Coronavirus detection and analysis on chest ct with deep learning. arXiv:200402640
  13. Kam H.T.: Random decision forest.. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, Canada, August, vol 1416, p 278282, 1995
  14. Kim S., Kim D.M., Lee B. (2020) Insufficient sensitivity of rna dependent rna polymerase gene of sars-cov-2 viral genome as confirmatory test using korean covid-19 cases · doi:10.20944/preprints202002.0424.v1
  15. Lewis D.D.: Naive (bayes) at forty: The independence assumption in information retrieval.. In: European conference on machine learning. Springer, New York, 1998, pp 4–15 · DOI · Paper page
  16. Li D., Wang D., Dong J., Wang N., Huang H., Xu H., Xia C.: False-negative results of real-time reverse-transcriptase polymerase chain reaction for severe acute respiratory syndrome coronavirus 2: Role of deep-learning-based ct diagnosis and insights from two cases. Korean journal of radiology 21 (4): 505–508, 2020a · DOI · Paper page
  17. Li Z., Yi Y., Luo X., Xiong N., Liu Y., Li S., Sun R., Wang Y., Hu B., Chen W., et al. (2020c) Development and clinical application of a rapid igm-igg combined antibody test for sars-cov-2 infection diagnosis. Journal of medical virology · DOI · Paper page
  18. Lippi G., Plebani M. (2020) Laboratory abnormalities in patients with covid-2019 infection. Clinical Chemistry and Laboratory Medicine (CCLM)1(ahead-of-print) · DOI · Paper page
  19. Lippi G., Simundic A.M., Plebani M. (2020) Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (covid-19). Clinical Chemistry and Laboratory Medicine (CCLM) 1(ahead-of-print) · DOI · Paper page
  20. MP C., et al., PJ (2020) Diagnostic testing for severe acute respiratory syndrome–related coronavirus-2: A narrative review. Annals of Internal Medicine. https://doi.org/10.7326/M20-1301 · DOI · Paper page
  21. Pan F., Ye T., Sun P., Gui S., Liang B., Li L., Zheng D., Wang J., Hesketh R.L., Yang L., et al. (2020) Time course of lung changes on chest ct during recovery from 2019 novel coronavirus (covid-19) pneumonia. Radiology p 200370 · DOI · Paper page
  22. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., et al.: Scikit-learn:, Machine learning in python. The Journal of machine Learning research 12: 2825–2830, 2011
  23. Qi Y.: Random forest for bioinformatics.. In: Ensemble machine learning. Springer, New York, 2012, pp 307–323 · DOI · Paper page
  24. Qin C., Zhou L., Hu Z., Zhang S., Yang S., Tao Y., Xie C., Ma K., Shang K., Wang W., et al. (2020) Dysregulation of immune response in patients with covid-19 in wuhan, china. China (February 17, 2020) · DOI · Paper page
  25. Rubino S., Kelvin N., Bermejo-Martin J.F., Kelvin D.: As covid-19 cases, deaths and fatality rates surge in italy, underlying causes require investigation. The Journal of Infection in Developing Countries 14 (03): 265–267, 2020 · DOI
  26. Safavian S.R., Landgrebe D.: A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics 21 (3): 660–674, 1991 · DOI · Paper page
  27. Schölkopf B, Smola A.J., Bach F., et al., 2002 Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press · DOI · Paper page
  28. Skurichina M., Duin R.P.W.: Stabilizing classifiers for very small sample sizes.. In: Proceedings of ICPR-1996, vol 2, 1996, pp 891–896 · doi:10.1109/icpr.1996.547204
  29. Wang D., Hu B., Hu C., Zhu F., Liu X., Zhang J., Wang B., Xiang H., Cheng Z., Xiong Y., et al: Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in wuhan, china. Jama 323 (11): 1061–1069, 2020 · DOI · Paper page
  30. Mea W. (2020) Chest x-ray findings in 636 ambulatory patients with covid-19 presenting to an urgent care center: a normal chest x-ray is no guarantee. The Journal of Urgent Care Medicin (2):1–9
  31. Zhang C., Shi L., Wang F.S. (2020) Liver injury in covid-19: management and challenges. The Lancet Gastroenterology & Hepatology · DOI · Paper page
  32. Zhao D., Yao F., Wang L., Zheng L., Gao Y., Ye J., Guo F., Zhao H., Gao R. (2020) A comparative study on the clinical features of COVID-19 pneumonia to other pneumonias. Clinical Infectious Diseases. https://doi.org/10.1093/cid/ciaa247 https://academic.oup.com/cid/article-pdf/doi/10.1093/cid/ciaa247/32894214/ciaa247.pdf · DOI · Paper page

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Metadata

Authors
Brinati, D., Campagner, A., Ferrari, D., Locatelli, M., Banfi, G., & Cabitza, F.
Venue
Journal of Medical Systems
Publisher
Springer Science and Business Media LLC
Published
2020-07-01
Volume
44
Issue
8
Pages
135-135
ISSN
  • 0148-5598 (ISSN-L)
  • 1573-689X
DOI
10.1007/s10916-020-01597-4
OpenAlex
W3038925693
Cited by
362
References
17
FWCI
8.42
Type
journal article
Language
en
Open access
bronze
Licence

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CoChat. (2026). Citation check: Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. https://app.cochat.ai/papers/10.1007/s10916-020-01597-4
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Brinati, D., Campagner, A., Ferrari, D., Locatelli, M., Banfi, G., & Cabitza, F. (2020). Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. Journal of Medical Systems. https://doi.org/10.1007/s10916-020-01597-4

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