Data-Driven Inference of COVID-19 Clinical Prognosis

  1. Joaquín Salas
  2. Dagoberto Pulido
  3. Omar Montoya
  4. Isaac Ruiz

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Abstract

Knowing the most likely clinical prognosis for a patient infected with SARS-Cov-2 could offer guidelines for tracking their medical evolution, improving attention, and assigning resources. Aiming to assess a patient’s status quantitatively, we explore the analysis of existing clinical information using data-driven methods. Our goal is to extract the characteristics distinguishing between those COVID-19 patients that improve and those who die. In our approach, we select the relevant features using the algorithm of Boruta, a wrapper framework that takes input from classifiers generating relevance assessment of the predictors. Using the extracted features, we train machine learning classifiers, including Random Forests, Support Vector Machine, Extreme Gradient Boosting, and Neural Networks. We assess the performance of the classifiers using Precision-Recall and ROC analysis, establishing the ranges at which risk assessment permits effective decision-making. Our research highlights that local regions present unique sets of essential features, that it is possible to construct effective classifiers based on clinical data, and that an ensemble of classifiers results in the best performing discriminant.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.27.20183202: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    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.

    About SciScore

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  2. Version published to 10.1101/2020.08.27.20183202v1 on medRxiv