BAND: Biomedical Alert News Dataset

TL;DR

The BAND dataset provides a large, well-annotated collection of biomedical outbreak alerts and news, enabling improved NLP models for epidemiological analysis and disease outbreak understanding.

Contribution

This paper introduces the BAND dataset, the largest annotated corpus of biomedical outbreak news with epidemiology-related questions, facilitating advanced NLP research in disease surveillance.

Findings

Existing models can handle NER, QA, and Event Extraction tasks in epidemiology.

The dataset reveals challenges in content disguise and inference in outbreak news.

Benchmark results highlight areas for future model improvements.

Abstract

Infectious disease outbreaks continue to pose a significant threat to human health and well-being. To improve disease surveillance and understanding of disease spread, several surveillance systems have been developed to monitor daily news alerts and social media. However, existing systems lack thorough epidemiological analysis in relation to corresponding alerts or news, largely due to the scarcity of well-annotated reports data. To address this gap, we introduce the Biomedical Alert News Dataset (BAND), which includes 1,508 samples from existing reported news articles, open emails, and alerts, as well as 30 epidemiology-related questions. These questions necessitate the model's expert reasoning abilities, thereby offering valuable insights into the outbreak of the disease. The BAND dataset brings new challenges to the NLP world, requiring better disguise capability of the content and the ability to infer important information. We provide several benchmark tasks, including Named Entity Recognition (NER), Question Answering (QA), and Event Extraction (EE), to show how existing models are capable of handling these tasks in the epidemiology domain. To the best of our knowledge, the BAND corpus is the largest corpus of well-annotated biomedical outbreak alert news with elaborately designed questions, making it a valuable resource for epidemiologists and NLP researchers alike.