蚊子传播的疾病正在全球范围内出现和重新出现,尤其是在COVID19大流行之后。传染病文本挖掘的最新进展具有提供及时访问文本中信息之间的显式和隐式关联的潜力。在过去的几年里,非结构化或半结构化文本形式的在线文本数据的可用性,具有来自该领域的丰富信息内容,使许多研究能够提供该领域的解决方案,例如,疾病相关知识发现,疾病监测,早期检测系统,等。然而,据我们所知,最近对蚊子传播疾病领域的文本挖掘进行了审查。在这次审查中,我们调查了用于防治蚊媒疾病的文本挖掘技术的最新工作。我们突出了语料库的来源,技术,应用程序,以及研究面临的挑战,其次是在这个领域可以进一步采取的未来可能的方向。我们对在Scopus和PubMed上发表的294篇有关蚊媒疾病文本挖掘领域的科学论文进行了文献计量分析,从2016年到2021年。根据用于分析与蚊子传播疾病有关的文本的技术,对论文进行了进一步的过滤和审查。根据158篇精选文章的语料库,我们发现其中27篇文章是相关的,并使用了蚊子传播疾病的文本挖掘。这些文章涵盖了寨卡病毒的大部分(38.70%),登革热(32.26%),疟疾(29.03%),数量极低或没有基孔肯雅热等其他重要的蚊媒疾病,黄热病,西尼罗河热.Twitter是在蚊媒疾病中进行文本挖掘的主要语料库资源,其次是PubMed和LexisNexis数据库。情感分析是最流行的文本挖掘技术,可以理解疾病的话语,其次是信息提取,其中依赖关系和基于共现的方法来提取关系和事件。监测是大多数审查研究的主要用途,其次是治疗,重点关注药物-疾病或症状-疾病的关联。文本挖掘的进步可以改善蚊媒疾病的管理。然而,该技术和应用带来了许多限制和挑战,包括用户身份验证和语言等偏见,现实世界的实现,等。我们讨论了未来的方向,这可能是有用的扩展这一领域和领域。这篇综述论文主要作为蚊子传播疾病文本挖掘的图书馆,并可以进一步探索其他被忽视疾病的系统。
Mosquito-borne diseases are emerging and re-emerging across the globe, especially after the COVID19 pandemic. The recent advances in text mining in infectious diseases hold the potential of providing timely access to explicit and implicit associations among information in the text. In the past few years, the availability of online text data in the form of unstructured or semi-structured text with rich content of information from this domain enables many studies to provide solutions in this area, e.g., disease-related knowledge discovery, disease surveillance, early detection system, etc. However, a recent
review of text mining in the domain of mosquito-borne disease was not available to the best of our knowledge. In this
review, we survey the recent works in the text mining techniques used in combating mosquito-borne diseases. We highlight the corpus sources, technologies, applications, and the challenges faced by the studies, followed by the possible future directions that can be taken further in this domain. We present a bibliometric analysis of the 294 scientific articles that have been published in Scopus and PubMed in the domain of text mining in mosquito-borne diseases, from the year 2016 to 2021. The papers were further filtered and reviewed based on the techniques used to analyze the text related to mosquito-borne diseases. Based on the corpus of 158 selected articles, we found 27 of the articles were relevant and used text mining in mosquito-borne diseases. These articles covered the majority of Zika (38.70%), Dengue (32.26%), and Malaria (29.03%), with extremely low numbers or none of the other crucial mosquito-borne diseases like chikungunya, yellow fever, West Nile fever. Twitter was the dominant corpus resource to perform text mining in mosquito-borne diseases, followed by PubMed and LexisNexis databases. Sentiment analysis was the most popular technique of text mining to understand the discourse of the disease and followed by information extraction, which dependency relation and co-occurrence-based approach to extract relations and events. Surveillance was the main usage of most of the reviewed studies and followed by treatment, which focused on the drug-disease or symptom-disease association. The advance in text mining could improve the management of mosquito-borne diseases. However, the technique and application posed many limitations and challenges, including biases like user authentication and language, real-world implementation, etc. We discussed the future direction which can be useful to expand this area and domain. This
review paper contributes mainly as a library for text mining in mosquito-borne diseases and could further explore the system for other neglected diseases.