@inbook{4eaf55bd20ce477b99f3f94acbf5a22d,
title = "DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information",
abstract = "Drug safety research is crucial for maintaining public health, often requiring comprehensive data support. However, the resources currently available to the public are limited and fail to provide a comprehensive understanding of the relationship between drugs and their side effects. This paper introduces DrugWatch, an easy-to-use and interactive multi-source information visualisation platform for drug safety study. It allows users to understand common side effects of drugs and their statistical information, flexibly retrieve relevant medical reports, or annotate their own medical texts with our automated annotation tool. Supported by NLP technology and enriched with interactive visual components, we are committed to providing researchers and practitioners with a one-stop information analysis, retrieval, and annotation service. The demonstration video is available at https://www.youtube.com/watch?v=RTqDgxzETjw. We also deployed an online demonstration system at https://drugwatch.net/.",
author = "Artem Bobrov and Domantas Saltenis and Zhaoyue Sun and Gabriele Pergola and Yulan He",
note = "Publisher Copyright: {\textcopyright} 2024 Association for Computational Linguistics.; 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 ; Conference date: 11-08-2024 Through 16-08-2024",
year = "2024",
language = "English",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "180--189",
editor = "Yixin Cao and Yang Feng and Deyi Xiong",
booktitle = "System Demonstrations",
}