Comparison of Different Models in Predicting COVID-19 Severity Based on Chest X-Ray Scans

The global outbreak of COVID-19 has resulted in a surge in patients in hospitals and intensive care units. This unprecedented demand for medical resources has severely burdened healthcare systems. Chest X-Ray (CXR) images can be used by hospitals and small clinics to predict COVID-19 severity to maximize efficiency and allot medical resources to patients with severe COVID-19. This research compares the accuracies of four convolutional neural network models in predicting COVID-19 severity using chest X-Rays images. The CNN models include VGG-16, ResNet 50, Xception, and a custom CNN model. Through the comparison, VGG-16 had the highest COVID-19 severity prediction accuracy of all four models, with 95.56% testing accuracy and 88.33% validation accuracy. Using a machine learning method, disease progression can be tracked more accurately and help prioritize patients to ensure effective and timely treatment. © 2022 IEEE.

COVID-19 Impact on Mental Health Analysis based on Reddit Comments

As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians. © 2022 IEEE.

COVID-19 Impact on Mental Health Analysis based on Reddit Comments

As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians. © 2022 IEEE.

An Advanced Machine Learning Algorithm for Intensive Care Decisions of COVID-19 Patients

COVID-19, an infectious respiratory disease, is a global health crisis and severely taxed healthcare systems. The SARS-CoV-2 virus damages lungs and other vital organs and even causes acute respiratory distress syndrome (ARDS). Currently, intensive care, including supplemental oxygen and ventilation, is used to treat severe cases. In this project, a Machine Learning algorithm was developed to predict intensive care needs for patients in the early stage of Covid-19. An advanced convolutional neural network (CNN) model was trained for image classification based on patient chest x-rays. After studying and comparing the performance of several advanced models, including Inception V3,ResNet50, Xception, EfficientNetB0, EfficientNetB7 and VGG16, It is identified that Inception V3showed the highest accuracy of the prediction. Based on Inception V3,an algorithm that demonstrates the highest accuracy of over 99% on both validation and testing datasets has been developed. The algorithm accurately makes predictions for which patients need immediate intensive care, so as to help the COVID19 patients' recovery and save more lives. © 2021 IEEE.

Comparison of Media Sources for COVID-19 by Machine Learning Sentiment Analysis

There is literature on Machine Learning Sentiment Analysis (MLSA) during the COVID-19 pandemic, however, to the best of our knowledge, there has been little to no research investigating the effectiveness of different internet media sources for the prediction of population-level sentiment;Twitter is currently the most often used in MLSA research. This study conducts COVID-19 related MLSA on various internet media sources to determine the relative effectiveness in each for mining public sentiment. The natural language processing is achieved through a long short-term memory (LSTM) neural network. By comparing trends of sentiment between social medias Twitter and Reddit, and news source USA Today with that of a control survey by data intelligence company Morning Consult, it is found Twitter has the lowest deviation in trends to that of the control. Assuming the objectivity of the control, Twitter is a better indicator of public sentiment as compared to Reddit and USA Today, capable for future applications of MLSA, especially when used in tandem with pre-existing surveys. This work helps advance research in MLSA with implications in informed decisions on fighting/recovering from COVID-19, flattening future potential pandemic curves, and indicating trends in public psychological and mental health. © 2021 IEEE.