Jeon Jouhyun, Baruah Gaurav, Sarabadani Sarah, Palanica Adam
Klick Labs, Klick Applied Sciences, Toronto, ON, Canada.
J Med Internet Res. 2020 Oct 2;22(10):e20509. doi: 10.2196/20509.
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.
2019年12月，新型冠状病毒肺炎疫情在中国爆发并迅速蔓延至全球。由于缺乏疫苗或优化的干预措施，确定风险因素和症状对于新型冠状病毒肺炎患者的早期识别和成功治疗变得至关重要。
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新型冠状病毒肺炎患者各种观察结果之间的关联。
通过语义分析，我们收集了45项回顾性队列研究，这些研究评估了新型冠状病毒肺炎患者13种不同结局的303个临床和人口统计学变量，以及来自1036名新型冠状病毒肺炎阳性用户的84140条推特帖子。引入机器学习工具来提取生物医学信息，以识别推文中不常见或新出现症状的提及。然后，我们检查并比较了两个数据集，以拓展我们对与新型冠状病毒肺炎相关的风险因素和症状的认识。
从生物医学文献来看，约90%的临床和人口统计学变量与新型冠状病毒肺炎结局的关联不一致。共识分析确定了72个与个体结局特异性相关的风险因素。从社交媒体数据中，我们对51种症状进行了特征描述和分析。通过将社交媒体数据与生物医学文献进行比较，我们识别出25种在推文中特别提到但之前未得到充分特征描述的新症状。此外，社交媒体中经常同时提及某些症状组合。
确定的特定结局风险因素、症状及症状组合可作为替代指标，用于识别新型冠状病毒肺炎患者并预测其临床结局，以便提供适当的治疗。
