【摘要】 2020年年初,新型冠状病毒感染的肺炎(COVID-19)爆发,中国采取了全面严格的防控举措全力抗击疫情.地方疫情指挥部门及时通报疫情感染数据,有助公众了解疫情的发展,及时做好防护措施.各地患者病例详情数据主要以文本形式记录,信息描述复杂,且各省市汇报的格式各异,处理难度较大.我们面向全国湖北省外近二分之一匿名的患者病例详情数据,提出了应用自然语言处理技术,辅助病例数据结构化的方法.该方法可以在标记样本较少的情况下,借助预训练模型,准确有效地提取出病例文本中的关键信息.通过对较大规模患者结构化病例数据的挖掘,本文详细分析了新型冠状肺炎总体发病性别和年龄分布特点、主要感染原因、潜伏期特点及疫情趋势等特征.由于潜伏期等时间延迟的存在,确诊人数往往不能反映一个地区的真实感染情况,结合出行大数据,本文提出了一个合理推断武汉市等城市实际感染人数的方法.该方法有助于人们提前估计地区疫情发展情况,及早采取防护措施.也可以辅助地方相关部门科学决策,尽早调度医务人员和分配医疗资源.更多还原

【Abstract】 In early 2020, the novel coronavirus, referred to as COVID-19 burst out. The Chinese people took the most comprehensive and rigorous control measures to fight against the COVID-19. Local health control departments reported infection data in a timely manner, which helped the public understand the development of the epidemic and take protective measures in advance. However, currently, no literature has analyzed the transmission characteristics of COVID-19 based on the structured data of large-scale patient cases and artificial intelligence. The detailed case data of patients in various regions are primarily recorded in text form, and the formats of report data in different provinces and cities differ, which makes it difficult to handle such data. To analysis around a large anonymous patient case data, we propose a method based on natural language processing technology to structure the case data. The proposed method can extract key information in the cases accurately and effectively with the help a pretrained model and a small number of labeled samples. By mining the patient’s structured case data, we analyze the gender and age distribution, the main causes of infection, the characteristics of the incubation period, and epidemic trends in detail. Using big data on travel, a method was developed to estimate the number of infected individuals in Wuhan prior the restrictions were put into effect. This method helps people understand the real epidemic situation and take execute early protective measures. It is also helps government departments make evidence-based decisions, dispatch medical staff, and allocate medical resources as quickly as possible.更多还原