DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information
Artem Bobrov, Domantas Saltenis, Zhaoyue Sun, Gabriele Pergola, Yulan He
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/.
BibTeX
@inproceedings{bobrov-etal-2024-drugwatch,
title = "{D}rug{W}atch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information",
author = "Bobrov, Artem and
Saltenis, Domantas and
Sun, Zhaoyue and
Pergola, Gabriele and
He, Yulan",
editor = "Cao, Yixin and
Feng, Yang and
Xiong, Deyi",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-demos.18/",
doi = "10.18653/v1/2024.acl-demos.18",
pages = "180--189"
}