Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Abstract

The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.

Latent Class Analysis Reveals COVID-19–related Acute Respiratory Distress Syndrome Subgroups with Differential Responses to Corticosteroids

“…The top 10 most important variables in the model are presented in Figure 4 and are consistent with those described in several other studies. [23][24][25] The several-fold higher importance values of age and troponin in comparison to other predictors in the model indicate that these two variables were the key predictors of mortality in this cohort. Among the top 10 predictor variables, age, compliance, glucose, INR, body mass index, and CT did not feature prominently as partitioning variables in the LCA model, suggesting that the prognostic value of the LCA classes is independent of common predictors of mortality in COVID-19-related ARDS.…”

Prediction of diagnosis and prognosis of COVID-19 disease by blood gas parameters using decision trees machine learning model: a retrospective observational study

“…28 Other studies using ML models found elevated neutrophil, CRP, lymphocyte and lactate dehydrogenase levels and advanced age variables as predictors of mortality associated with COVID-19 disease. 29 30…”

A Time-Series Feature-Based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients

“…Several machine learning models have been proposed to predict the outcome of COVID-19 patients [27][28][29][30][31]. However, most of the key features of these models are lab values and may not have the ability to provide early COVID-19 mortality prediction.…”

“…In terms of the performance of these models, most of their AUCs are between 90% and 95%, to which the performance of our model is comparable. Karthikeyan et al [31] proposed a model that performed with AUC of 99% when predicted at the day of outcome but performed worse when it predicted far from the day of outcome. This demonstrated the difficulty of early COVID-19 mortality prediction.…”