DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information

Artem Bobrov, Domantas Saltenis, Zhaoyue Sun, Gabriele Pergola, Yulan He

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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/.

Original languageEnglish
Title of host publicationSystem Demonstrations
EditorsYixin Cao, Yang Feng, Deyi Xiong
PublisherAssociation for Computational Linguistics (ACL)
Pages180-189
Number of pages10
ISBN (Electronic)9798891760967
Publication statusPublished - 2024
Event62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 - Bangkok, Thailand
Duration: 11 Aug 202416 Aug 2024

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
Volume3
ISSN (Print)0736-587X

Conference

Conference62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024
Country/TerritoryThailand
CityBangkok
Period11/08/202416/08/2024

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