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
Not cool, calm or collected: Using emotional language to detect COVID-19 misinformation
Gabriel Asher, Phil Bohlman, Karsten Kleyensteuber
Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping
Ryo Toda, Hayato Itoh, Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori
Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation
Muhammad Asad, Helena Williams, Indrajeet Mandal, Sarim Ather, Jan Deprest, Jan D'hooge, Tom Vercauteren
Human Behavior in the Time of COVID-19: Learning from Big Data
Hanjia Lyu, Arsal Imtiaz, Yufei Zhao, Jiebo Luo
COVID-19 event extraction from Twitter via extractive question answering with continuous prompts
Yuhang Jiang, Ramakanth Kavuluru
How People Respond to the COVID-19 Pandemic on Twitter: A Comparative Analysis of Emotional Expressions from US and India
Brandon Siyuan Loh, Raj Kumar Gupta, Ajay Vishwanath, Andrew Ortony, Yinping Yang