论文标题
通过机器学习和自然语言处理了解COVID-19的时间演变
Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing
论文作者
论文摘要
由严重的急性呼吸综合症2（SARS-COV-2）引起的新型冠状病毒疾病（COVID-19）的爆发一直在许多方面不断影响世界各地的人类生活和社区，从封锁的城市到新的社会经历。尽管在大多数情况下，由于SARS-COV-2的具有极高的传染性，因此Covid-19导致轻度疾病，但它引起了全球关注。政府和医疗保健专业人员以及整个人和社会都采取了任何措施打破过渡链，并使流行曲线变平。在这项研究中，我们使用了多种数据源，即PubMed和Arxiv，并建立了几种机器学习模型，以通过确定潜在主题并分析潜在的主题并分析提取的研究主题，出版物相似性和较大的观点，以确认我们的研究范围更大的研究，我们的研究中的范围较大的研究中的介绍。在COVID-19相关问题和后者方面，多样性更多地关注智能系统/工具，以预测/诊断COVID-19。研究界对高风险群体和并发症的人的特别关注也得到了证实。
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January- May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.
