ABSTRACT
BACKGROUND AND PURPOSE: COVID-19, which emerged in Wuhan (China), is one of the deadliest and fastest-spreading pandemics as of the end of 2019. According to the World Health Organization (WHO), there are more than 100 million infectious cases worldwide. Therefore, research models are crucial for managing the pandemic scenario. However, because the behavior of this epidemic is so complex and difficult to understand, an effective model must not only produce accurate predictive results but must also have a clear explanation that enables human experts to act proactively. For this reason, an innovative study has been planned to diagnose Troponin levels in the COVID-19 process with explainable white box algorithms to reach a clear explanation. METHODS: Using the pandemic data provided by Erzurum Training and Research Hospital (decision number: 2022/13-145), an interpretable explanation of Troponin data was provided in the COVID-19 process with SHApley Additive exPlanations (SHAP) algorithms. Five machine learning (ML) algorithms were developed. Model performances were determined based on training, test accuracies, precision, F1-score, recall, and AUC (Area Under the Curve) values. Feature importance was estimated according to Shapley values by applying the SHApley Additive exPlanations (SHAP) method to the model with high accuracy. The model created with Streamlit v.3.9 was integrated into the interface with the name CVD22. RESULTS: Among the five-machine learning (ML) models created with pandemic data, the best model was selected with the values of 1.0, 0.83, 0.86, 0.83, 0.80, and 0.91 in train and test accuracy, precision, F1-score, recall, and AUC values, respectively. As a result of feature selection and SHApley Additive exPlanations (SHAP) algorithms applied to the XGBoost model, it was determined that DDimer mean, mortality, CKMB (creatine kinase myocardial band), and Glucose were the features with the highest importance over the model estimation. CONCLUSIONS: Recent advances in new explainable artificial intelligence (XAI) models have successfully made it possible to predict the future using large historical datasets. Therefore, throughout the ongoing pandemic, CVD22 (https://cvd22covid.streamlitapp.com/) can be used as a guide to help authorities or medical professionals make the best decisions quickly.
Subject(s)
Artificial Intelligence , COVID-19 , Humans , Algorithms , Fibrin Fibrinogen Degradation ProductsABSTRACT
BACKGROUND: Coronavirus-19 is still considered a pandemic that influences the world. Other molecular alterations should be clearer besides the increasing cytokine storm and pro-inflammatory molecules. Hypoxic conditions that induce HIF-1α lead to stimulate gene expression of STC-2 that targets PAPP-A expression. This study aimed to determine gene expression levels of PAPP-A, STC-2, and HIF-1α in COVID-19 infection. We also aimed to reveal the relationship of these genes with laboratory and clinical data of COVID-19 patients. MATERIALS AND RESULTS: We extracted RNA from peripheral blood samples of COVID-19(+) and COVID-19(-) individuals. The real-time PCR method was used to measure mRNA expression of PAPP-A, STC-2, and HIF-1α. Gene expression analysis was evaluated by the 2-ΔΔCt method. PAPP-A, STC-2, and HIF-1α mRNA expressions of severe patients were higher than healthy individuals (p = 0.0451, p = 0.4466, p < 0.0001, respectively). Correlation analysis of gene expression patterns of severe patients demonstrated a positive correlation between PAPP-A and STC-2 (p < 0.0001, r = 0.8638). CONCLUSION: This is the first study that investigates the relation of PAPP-A, STC-2, and HIF-1α gene expression in patients with COVID-19 infection. Besides the routine laboratory findings, PAPP-A, STC-2, and HIF-1α mRNA expressions may be considered to patients' prognosis as a sign of increased cytokines and pro-inflammatory molecules.