Identification of Risk Factors and Symptoms of COVID-19: Analysis of Biomedical Literature and Social Media Data.
Identification of Risk Factors and Symptoms of COVID-19: Analysis of Biomedical Literature and Social Media Data.
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DOI：
10.2196/20509
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发表时间：
2020-10-02
影响因子：
7.4
通讯作者：
Palanica A
中科院分区：
文献类型：
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作者：
Jeon J;Baruah G;Sarabadani S;Palanica A
2019年12月，COVID-19疫情从中国开始并迅速蔓延至全球。由于缺乏疫苗或优化的干预措施，描述风险因素和症状对于早期识别和成功治疗COVID-19患者的重要性提高了。本研究旨在调查和分析生物医学文献和公共社交媒体数据，以了解风险因素和症状与COVID-19患者中观察到的各种结局之间的关联。通过语义分析，我们收集了45项回顾性队列研究，这些研究评估了COVID-19患者13种不同结局的303个临床和人口统计学变量，以及来自1036名COVID-19阳性用户的84，140条Twitter帖子。引入了提取生物医学信息的机器学习工具，以识别推文中提到的不常见或新症状。然后，我们检查并比较了两个数据集，以扩大我们与COVID-19相关的风险因素和症状的范围。从生物医学文献来看，约90%的临床和人口统计学变量与COVID-19结果的相关性不一致。共识分析确定了72个与个体结局特别相关的风险因素。从社交媒体数据中，对51种症状进行了描述和分析。通过将社交媒体数据与生物医学文献进行比较，我们确定了25种新的症状，这些症状在推文中特别提到，但以前没有得到很好的表征。此外，社交媒体上经常提到某些症状的组合。已识别的结果特异性风险因素、症状和症状组合可作为替代指标，用于识别COVID-19患者并预测其临床结果，以便提供适当的治疗。
In December 2019, the COVID-19 outbreak started in China and rapidly spread around the world. Lack of a vaccine or optimized intervention raised the importance of characterizing risk factors and symptoms for the early identification and successful treatment of patients with COVID-19. This study aims to investigate and analyze biomedical literature and public social media data to understand the association of risk factors and symptoms with the various outcomes observed in patients with COVID-19. Through semantic analysis, we collected 45 retrospective cohort studies, which evaluated 303 clinical and demographic variables across 13 different outcomes of patients with COVID-19, and 84,140 Twitter posts from 1036 COVID-19–positive users. Machine learning tools to extract biomedical information were introduced to identify mentions of uncommon or novel symptoms in tweets. We then examined and compared two data sets to expand our landscape of risk factors and symptoms related to COVID-19. From the biomedical literature, approximately 90% of clinical and demographic variables showed inconsistent associations with COVID-19 outcomes. Consensus analysis identified 72 risk factors that were specifically associated with individual outcomes. From the social media data, 51 symptoms were characterized and analyzed. By comparing social media data with biomedical literature, we identified 25 novel symptoms that were specifically mentioned in tweets but have been not previously well characterized. Furthermore, there were certain combinations of symptoms that were frequently mentioned together in social media. Identified outcome-specific risk factors, symptoms, and combinations of symptoms may serve as surrogate indicators to identify patients with COVID-19 and predict their clinical outcomes in order to provide appropriate treatments.