Rapid protocols to support Covid-19 clinical diagnosis based on hematological parameters

  1. Juliana Carneiro Gomes
  2. Valter Augusto de Freitas Barbosa
  3. Maíra Araújo de Santana
  4. Clarisse Lins de Lima
  5. Raquel Bezerra Calado
  6. Cláudio Roberto Bertoldo Júnior
  7. Jeniffer Emidio de Almeida Albuquerque
  8. Rodrigo Gomes de Souza
  9. Ricardo Juarez Escorel de Araújo
  10. Giselle Machado Magalhães Moreno
  11. Luiz Alberto Lira Soares
  12. Luiz Alberto Reis Mattos Júnior
  13. Ricardo Emmanuel de Souza
  14. Wellington Pinheiro dos Santos

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Abstract

Purpose

In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease.

Objective

In this work, we propose rapid protocols for clinical diagnosis of Covid-19 through the automatic analysis of hematological parameters using Evolutionary Computing and Machine Learning. These hematological parameters are obtained from blood tests common in clinical practice.

Method

We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis.

Results

We developed a web system for Covid-19 diagnosis support. Using a 100-tree Random Forest, we obtained results for accuracy, sensitivity and specificity superior to 99

Conclusion

By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system.

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    SciScore for 10.1101/2021.06.21.21259252: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.06.21.21259252v1 on medRxiv