JMIR Publications

ABSTRACT

Background:

Accurately differentiating severe from non-severe COVID-19 clinical types is critical for the healthcare system to optimize workflow. Current techniques lack the ability to accurately predict COVID-19 patients’ clinical type, especially as SARS-CoV-2 continues to mutate.

Objective:

We explore predictability and interpretability of multiple state-of-the-art machine learning (ML) techniques trained and tested under different biomedical data types and COVID-19 variants.

Methods:

Comprehensive patient-level data were collected from 362 patients (214 severe, 148 non-severe) with the original SARS-CoV-2 variant in 2020 and 1000 patients (500 severe, 500 non-severe) with the Omicron variant in 2022-2023. The data included 26 biochemical features from blood testing and 26 clinical features from patients’ clinical characteristics and medical history. Different ML techniques including penalized logistic regression (LR), random forest (RF), k-nearest neighbors (kNN), and support vector machines (SVM) were applied to build predictive models based on each data modality separately and together for each variant. Fifty randomized train-test-splits were conducted per scenario and performance results were recorded.

Results:

The fused (hybrid) characteristic modality yielded the highest mean area under the curve (AUC) achieving 0.915, while the biochemical modality alone and the clinical modality alone had AUCs of 0.862 and 0.818 respectively. All ML models performed similarly under different testing scenarios and were robust when cross-tested with original and Omicron variant patient data. Our models ranked elevated d-dimer (biochemical), elevated high sensitivity troponin I (biochemical), and age greater than 55 years (clinical) as the most predictive features of severe COVID-19.

Conclusions:

ML is a powerful tool for predicting severe COVID-19 based on comprehensive individual patient-level data. Further, ML models trained on the biochemical and clinical modalities together witness enhanced predictive power. The improved performance of these ML models when trained and cross-tested with Omicron variant data supports the robustness of ML as a tool for clinical decision support.