Abstract
One important task in the COVID-19 clinical protocol involves the constant monitoring of patients to detect possible signs of insufficiency, which may eventually rapidly progress to hepatic, renal or respiratory failures. Hence, a prompt and correct clinical decision not only is critical for patients prognosis, but also can help when making collective decisions regarding hospital resource management. In this work, we present a network-based high-level classification technique to help healthcare professionals on this activity, by detecting early signs of insufficiency based on Complete Blood Count (CBC) test results. We start by building a training dataset, comprising both CBC and specific tests from a total of 2,982 COVID-19 patients, provided by a Brazilian hospital, to identify which CBC results are more effective to be used as biomarkers for detecting early signs of insufficiency. Basically, the trained classifier measures the compliance of the test instance to the pattern formation of the network constructed from the training data. To facilitate the application of the technique on larger datasets, a network reduction option is also introduced and tested. Numerical results show encouraging performance of our approach when compared to traditional techniques, both on benchmark datasets and on the built COVID-19 dataset, thus indicating that the proposed technique has potential to help medical workers in the severity assessment of patients. Especially those who work in regions with scarce material resources.
This work was carried out at the Center for Artificial Intelligence (C4AI-USP), with support by the São Paulo Research Foundation (FAPESP) under grant number: 2019/07665-4 and by the IBM Corporation. This work is also supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, FAPESP under grant numbers 2015/50122-0, the Brazilian National Council for Scientific and Technological Development (CNPq) under grant number 303199/2019-9, and the Ministry of Science and Technology of China under grant number: G20200226015.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anghinoni, L., Zhao, L., Ji, D., Pan, H.: Time series trend detection and forecasting using complex network topology analysis. Neural Netw. 117, 295–306 (2019)
Beck, B.R., Shin, B., Choi, Y., Park, S., Kang, K.: Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput. Struct. Biotechnol. J. 18, 784–790 (2020)
Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Carneiro, M.G., Zhao, L.: Organizational data classification based on the importance concept of complex networks. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3361–3373 (2017)
Chen, D., Lü, L., Shang, M.S., Zhang, Y.C., Zhou, T.: Identifying influential nodes in complex networks. Phys. A: Stat. Mech. Appl. 391(4), 1777–1787 (2012)
Colliri, T., Ji, D., Pan, H., Zhao, L.: A network-based high level data classification technique. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)
Csardi, G., Nepusz, T., et al.: The Igraph software package for complex network research. Int. J. Complex Syst. 1695(5), 1–9 (2006)
Fapesp: Research data metasearch. https://repositorio.uspdigital.usp.br/handle/item/243. Accessed 1 Feb 2021
Freund, Yoav, Schapire, Robert E..: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, Paul (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59119-2_166
Gelman, A., Hill, J.: Data Analysis Using Regression and Multilevel Hierarchical Models, vol. 1. Cambridge University Press, New York (2007)
Hinton, G.E.: Connectionist learning procedures. Artif. Intell. 40(1–3), 185–234 (1989)
Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Metsky, H.C., Freije, C.A., Kosoko-Thoroddsen, T.S.F., Sabeti, P.C., Myhrvold, C.: CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design. BioRxiv (2020)
Pearson, K.: Note on regression and inheritance in the case of two parents. Proc. Royal Soc. London 58(347–352), 240–242 (1895)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Rish, I.: An empirical study of the Naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3(22) (2001), IBM New York
Safavin, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst., Man, Cybern. 21(3), 660–674 (1991)
Secr. Saude SP: prefeitura.sp.gov.br/cidade/secretarias/saude/vigilancia\(\_\)em\(\_\)saude/. Accessed 9 May 2021
Shan, F., et al.: Lung infection quantification of COVID-19 in CT images with deep learning. arXiv preprint arXiv:2003.04655 (2020)
Silva, T.C., Zhao, L.: Network-based high level data classification. IEEE Trans. Neural Netw. Learn. Syst. 23(6), 954–970 (2012)
Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001)
Valejo, A., Ferreira, V., Fabbri, R., Oliveira, M.C.F.d., Lopes, A.D.A.: A critical survey of the multilevel method in complex networks. ACM Comput. Surv. (CSUR) 53(2), 1–35 (2020)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (2000). https://doi.org/10.1007/978-1-4757-3264-1
Yan, L., et al.: An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. 2(5), 1–6 (2020)
Zoabi, Y., Deri-Rozov, S., Shomron, N.: Machine learning-based prediction of COVID-19 diagnosis based on symptoms. npj Digital Med. 4(1), 1–5 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Colliri, T., Minakawa, M., Zhao, L. (2021). Detecting Early Signs of Insufficiency in COVID-19 Patients from CBC Tests Through a Supervised Learning Approach. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13074. Springer, Cham. https://doi.org/10.1007/978-3-030-91699-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-91699-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91698-5
Online ISBN: 978-3-030-91699-2
eBook Packages: Computer ScienceComputer Science (R0)