全球化和环境变化加剧了世界范围内传染病的出现或重新出现,比如东南亚爆发的登革热。因此,在全区域传染病监测系统上的合作至关重要,但由于不同国家卫生信息系统的透明度不同,难以实现。尽管新兴疾病监测计划(ProMED)-mail是最全面的国际专家策划平台,提供有关人类的丰富疾病爆发信息,动物,和植物,报告的非结构化文本内容使得进一步应用的分析变得困难。
为了更有效地监测东南亚的疫情,这项研究旨在开发来自ProMED-mail的警报文章的自动摘要,一个巨大的文本数据源。在本文中,我们提出了一种文本摘要方法,该方法使用自然语言处理技术从ProMED-mail电子邮件中的警报文章中自动提取重要句子以生成摘要。使用我们的方法,我们可以快速捕获关键信息,以帮助做出有关流行病监测的重要决策。
我们的数据,从1994年到2019年,来自ProMED-mail网站。我们分析了收集的数据,以建立一个独特的台湾登革热语料库,并通过专业人士的注释进行了验证,以达到几乎完美的一致性(Cohenκ=90%)。要生成ProMED邮件摘要,我们开发了一种带有注意力机制的双通道双向长短期记忆,并注入了潜在的句法特征,以从警报文章中识别关键句子。
我们的方法在识别重要句子方面优于许多众所周知的机器学习和神经网络方法,达到93%的大平均F1分数。此外,它可以从ProMED邮件警报文章中成功提取有关登革热的正确信息,这可以帮助研究人员或一般用户快速理解警报文章的本质。除了验证模型之外,我们还招募了3名专业专家和2名相关领域的学生参加了对生成的总结的满意度调查,结果显示,84%(63/75)的摘要获得了很高的满意度。
所提出的方法成功地将潜在的句法特征融合到深度神经网络中,以分析句法,语义,和文本中的上下文信息。然后,它利用派生的信息来识别ProMED邮件警报文章中的关键句子。实验结果表明,该方法不仅有效,而且优于对比方法。我们的方法还展示了从ProMED邮件警报文章中生成案例摘要的潜力。从实际应用来看,当新的警报文章到达时,我们的方法可以快速识别相关病例信息,这是最关键的部分,用作参考或进一步分析。
Globalization and environmental changes have intensified the emergence or re-emergence of infectious diseases worldwide, such as outbreaks of dengue fever in Southeast Asia. Collaboration on region-wide infectious disease surveillance systems is therefore critical but difficult to achieve because of the different transparency levels of health information systems in different countries. Although the Program for Monitoring Emerging Diseases (ProMED)-mail is the most comprehensive international expert-curated platform providing rich disease outbreak information on humans, animals, and plants, the unstructured text content of the
reports makes analysis for further application difficult.
To make monitoring the epidemic situation in Southeast Asia more efficient, this study aims to develop an automatic summary of the alert articles from ProMED-mail, a huge textual data source. In this paper, we proposed a text summarization method that uses natural language processing technology to automatically extract important sentences from alert articles in ProMED-mail emails to generate summaries. Using our method, we can quickly capture crucial information to help make important decisions regarding epidemic surveillance.
Our data, which span a period from 1994 to 2019, come from the ProMED-mail website. We analyzed the collected data to establish a unique Taiwan dengue corpus that was validated with professionals\' annotations to achieve almost perfect agreement (Cohen κ=90%). To generate a ProMED-mail summary, we developed a dual-channel bidirectional long short-term memory with attention mechanism with infused latent syntactic features to identify key sentences from the alerting article.
Our method is superior to many well-known machine learning and neural network approaches in identifying important sentences, achieving a macroaverage F1 score of 93%. Moreover, it can successfully extract the relevant correct information on dengue fever from a ProMED-mail alerting article, which can help researchers or general users to quickly understand the essence of the alerting article at first glance. In addition to verifying the model, we also recruited 3 professional experts and 2 students from related fields to participate in a satisfaction survey on the generated summaries, and the results show that 84% (63/75) of the summaries received high satisfaction ratings.
The proposed approach successfully fuses latent syntactic features into a deep neural network to analyze the syntactic, semantic, and contextual information in the text. It then exploits the derived information to identify crucial sentences in the ProMED-mail alerting article. The experiment results show that the proposed method is not only effective but also outperforms the compared methods. Our approach also demonstrates the potential for
case summary generation from ProMED-mail alerting articles. In terms of practical application, when a new alerting article arrives, our method can quickly identify the relevant
case information, which is the most critical part, to use as a reference or for further analysis.