Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing.
Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing.
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DOI：
10.1007/s11192-020-03744-7
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Wong A
中科院分区：
文献类型：
--
作者：
Ebadi A;Xi P;Tremblay S;Spencer B;Pall R;Wong A
由严重急性呼吸道综合征冠状病毒2型（SARS-CoV-2）引起的2019新型冠状病毒病（COVID-19）的爆发，从城市封锁到新的社会体验，在许多方面持续影响着世界各地的人类生活和社区。虽然在大多数情况下，COVID-19导致轻微疾病，但由于SARS-CoV-2的极强传染性，它引起了全球的关注。各国政府和医护人员，连同民众和整个社会，沿着，采取了一切措施来打破转移链，使疫情曲线变平。在这项研究中，我们使用了多个数据源，即，PubMed和ArXiv，并在2020年1月至5月的时间范围内，通过识别潜在主题并分析提取的研究主题、出版物相似性和情绪的时间演变，建立了几个机器学习模型来表征当前COVID-19研究的格局。我们的研究结果证实，PubMed和ArXiv的研究类型存在显著差异，前者在COVID-19相关问题方面表现出更大的多样性，后者更侧重于预测/诊断COVID-19的智能系统/工具。研究界对高风险群体和并发症患者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.
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影响因子：
4.6
作者：
Wang L;Lin ZQ;Wong A
通讯作者：
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影响因子：
3.1
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