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
5q032e@SMM4H'22: Transformer-based classification of premise in tweets related to COVID-19
Vadim Porvatov, Natalia Semenova
Developing a multi-variate prediction model for the detection of COVID-19 from Crowd-sourced Respiratory Voice Data
Wafaa Aljbawi, Sami O. Simmons, Visara Urovi
Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application
Andrei Velichko, Mehmet Tahir Huyut, Maksim Belyaev, Yuriy Izotov, Dmitry Korzun
Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake News
Mohammad Majid Akhtar, Bibhas Sharma, Ishan Karunanayake, Rahat Masood, Muhammad Ikram, Salil S. Kanhere
Taking a Language Detour: How International Migrants Speaking a Minority Language Seek COVID-Related Information in Their Host Countries
Ge Gao, Jian Zheng, Eun Kyoung Choe, Naomi Yamashita