Covid 19
COVID-19 research continues to explore the pandemic's multifaceted impact, focusing on accurate prediction of disease severity and mortality, effective diagnosis and treatment strategies, and understanding the spread of misinformation. Current research leverages machine learning, particularly deep learning models like convolutional neural networks and large language models, to analyze diverse data sources including chest X-rays, blood test parameters, and social media posts. These efforts aim to improve clinical decision-making, enhance public health interventions, and ultimately mitigate the long-term consequences of the pandemic.
Papers
Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron
Janhavi Lande, Arti Pillay, Rohitash Chandra
PANACEA: An Automated Misinformation Detection System on COVID-19
Runcong Zhao, Miguel Arana-Catania, Lixing Zhu, Elena Kochkina, Lin Gui, Arkaitz Zubiaga, Rob Procter, Maria Liakata, Yulan He
Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis
Brianna M White, Chad A Melton, Parya Zareie, Robert L Davis, Robert A Bednarczyk, Arash Shaban-Nejad
Exploring Social Media for Early Detection of Depression in COVID-19 Patients
Jiageng Wu, Xian Wu, Yining Hua, Shixu Lin, Yefeng Zheng, Jie Yang
Using natural language processing and structured medical data to phenotype patients hospitalized due to COVID-19
Feier Chang, Jay Krishnan, Jillian H Hurst, Michael E Yarrington, Deverick J Anderson, Emily C O'Brien, Benjamin A Goldstein
Causal Confirmation Measures: From Simpson's Paradox to COVID-19
Chenguang Lu