Global and local interpretation of black-box machine learning models to determine prognostic factors from early COVID-19 data

Ananya Jana; Carlos D. Minacapelli; Vinod Rustgi; Dimitris Metaxas

doi:10.1117/12.2604743

10 December 2021 Global and local interpretation of black-box machine learning models to determine prognostic factors from early COVID-19 data

Ananya Jana, Carlos D. Minacapelli, Vinod Rustgi, Dimitris Metaxas

Author Affiliations +

Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 120880A (2021) https://doi.org/10.1117/12.2604743
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil

Abstract

The COVID-19 corona virus has claimed 4.1 million lives, as of July 24, 2021. A variety of machine learning models have been applied to related data to predict important factors such as the severity of the disease, infection rate and discover important prognostic factors. Often the usefulness of the findings from the use of these techniques is reduced due to lack of method interpretability. Some recent progress made on the interpretability of machine learning models has the potential to unravel more insights while using conventional machine learning models.1–3 In this work, we analyze COVID-19 blood work data with some of the popular machine learning models; then we employ state-of-the-art post-hoc local interpretability techniques(e.g.- SHAP, LIME), and global interpretability techniques(e.g. - symbolic metamodeling) to the trained black-box models to draw interpretable conclusions. In the gamut of machine learning algorithms, regressions remain one of the simplest and most explainable models with clear mathematical formulation. We explore one of the most recent techniques called symbolic metamodeling to find the mathematical expression of the machine learning models for COVID-19. We identify Acute Kidney Injury (AKI), initial Albumin level (ALB I), Aspartate aminotransferase (AST I), Total Bilirubin initial (TBILI) and D-Dimer initial (DIMER) as major prognostic factors of the disease severity. Our contributions are - (i) uncover the underlying mathematical expression for the black-box models on COVID-19 severity prediction task (ii) we are the first to apply symbolic metamodeling to this task, and (iii) discover important features and feature interactions. Code repository: https://github.com/ananyajana/interpretable covid19.

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Ananya Jana, Carlos D. Minacapelli, Vinod Rustgi, and Dimitris Metaxas "Global and local interpretation of black-box machine learning models to determine prognostic factors from early COVID-19 data", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 120880A (10 December 2021); https://doi.org/10.1117/12.2604743

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KEYWORDS

Data modeling

Mathematical modeling

Machine learning

Statistical modeling

Blood

Kidney

Chest

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