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
Objective: The aim of this study is to investigate the anxiety, depression, insomnia and post traumatic stress disorder (PTSD) symptoms and the assocaited sociodemographic, clinical and professional factors during the COVID-19 pandemic in healthhcare workers. Method: A total of 509 participants joined an online survey to complete the data acquisition tools consisting of a Sociodemographic and Clinical Questionnaire, the Hospital Anxiety and Depression Scale (HADS), the Insomnia Severity Index (ISI) and the Post Traumatic Stress Disorder- Short Scale (PTSD-SS). Results: The 509 participants of the study consisted of physicians (69.2%) and nurses (30.8%). On the basis of the scores above the cut-off points of the pscyhometric scales used, the mental symptoms of the participants were ranked as 54.2% on depression, 26.3% on anxiety, 20.8% on insomnia and 8.8% on PTSD. The corresponding scores of the 20-30 year old, the female and the nursing participants were significantly higher as compared to the others (p<0.001, for all). Significant differences were not found in these scores with respect to working or not working directly with COVID-19 patients, or having a family member with or without COVID-19 infection (p>0.05). Having a history of suspected COVID-19 infection was significantly associated with insomnia (p=0.026 and PTSD (p=0.008). Also, the anxiety and PTSD scores of the participants with a history of mental disorder diagnosis were significantly higher in comparison to the others (p<0.001). Conclusion: The results indicated that females, nurses, participants in the 20-30 year age group and with a history of mental disorder diagnosis were in the high risk group for impaired mental health, irrespective of their professional positions. Close monitoring and early intervention are essential for these high-risk individuals.