This paper describes the web-based automated disease-related topic extraction system, called to DiTeX, which monitors important disease-related topics and provides associated information. National disease surveillance systems require a considerable amount of time to inform people of recent outbreaks of diseases. To solve this problem, many studies have used Internet-based sources such as news and Social Network Service (SNS). However, these sources contain many intentional elements that disturb extracting important topics. To address this challenge, we employ Natural Language Processing and an effective ranking algorithm, and develop DiTeX that provides important disease-related topics. This report describes the web front-end and back-end architecture, implementation, performance of the ranking algorithm, and captured topics of DiTeX. We describe processes for collecting Internet-based data and extracting disease-related topics based on search keywords. Our system then applies a ranking algorithm to evaluate the importance of disease-related topics extracted from these data. Finally, we conduct analysis based on real-world incidents to evaluate the performance and the effectiveness of DiTeX. To evaluate DiTeX, we analyze the ranking of well-known disease-related incidents for various ranking algorithms. The topic extraction rate of our ranking algorithm is superior to those of others. We demonstrate the validity of DiTeX by summarizing the disease-related topics of each day extracted by our system. To our knowledge, DiTeX is the world’s first automated web-based real-time service system that extracts and presents disease-related topics, trends and related data through web-based sources. DiTeX is now available on the web through http://epidemic.co.kr/media/topics.
Bibliographical noteFunding Information:
This work was supported by a National Research Foundation of Korea grant funded by the Korea government (2016R1D1A1B03930815).
© 2018 Yoon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
All Science Journal Classification (ASJC) codes
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)