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Detecting Early Signs of Insufficiency in COVID-19 Patients from CBC Tests Through a Supervised Learning Approach

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Intelligent Systems (BRACIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13074))

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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.

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Authors and Affiliations

  1. Dept. of Computer Science, University of Fortaleza (UNIFOR), Fortaleza, Brazil

    Tiago Colliri

  2. Faculty of Public Health, Univ. of Sao Paulo, Sao Paulo, Brazil

    Marcia Minakawa

  3. Faculty of Philosophy, Science and Letters, Univ. of Sao Paulo, Ribeirao Preto, Brazil

    Liang Zhao

Authors
  1. Tiago Colliri
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  2. Marcia Minakawa
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  3. Liang Zhao
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Corresponding author

Correspondence to Tiago Colliri .

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Editors and Affiliations

  1. Universidade Federal de Sergipe, São Cristóvão, Brazil

    André Britto

  2. Universidade de São Paulo, São Paulo, Brazil

    Karina Valdivia Delgado

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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

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  • DOI: https://doi.org/10.1007/978-3-030-91699-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91698-5

  • Online ISBN: 978-3-030-91699-2

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