Given the volume of data available on COVID-19, the research community and health care professionals need to have access to timely information as soon as it is available in the literature. COVID-19 researchers are facing a significant challenge in sifting through a large body of literature to find relevant and credible information. In less than two years, there has been an explosion of literature on COVID-19 (since its inception). In this study, we focus on the ongoing pandemic issue and propose an artificial intelligence (AI) solution that can be easily extended and adapted to quickly learn relevant insights critical for understanding and combating any new infectious disease. As of July 2021, the “long-COVID” symptoms, also known as post-COVID conditions, are classified as a disability under the Americans with Disabilities Act (ADA). A significant number of patients with COVID-19 also report prolonged symptoms, known as long-COVID, which can damage the lungs, heart and brain increasing the risk of long-term health problems. It has also increased mental health issues such as depression, post-traumatic stress disorder, and suicide. COVID-19 has caused many physical complications, such as pneumonia, acute respiratory distress syndrome (ARDS), multi-organ failure, and death. It has been reported in about 200 countries and territories, with more than 266 million cases reported around the world and 5.6 million deaths. COVID-19 has affected a lot of people all over the world. It also outperforms other benchmark datasets, demonstrating the generalizability of the proposed approach.Ĭoronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. The proposed model has outperformed previous models, obtaining an exact match ratio score of 71.45% and a semantic answer similarity score of 78.55%. It also consists of a reader component that consists of a Transformer-based model, which is used to read the paragraphs and find the answers related to the query from the retrieved documents. The CO-SE has a retriever component trained on the TF–IDF vectorizer that retrieves the relevant documents from the system. This problem motivates us to propose the design of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that finds relevant documents for each query (asked by a user) and answers complex questions by searching a large corpus of publications. However, there are a lot of research papers being written on the subject, which makes it hard to keep up with the most recent research. Practitioners, front-line workers, and researchers require expert-specific methods to stay current on scientific knowledge and research findings. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus.
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