Advancing Biomedical Text Mining with Community Challenges
Hui Zong Rongrong Wu Jiaxue Cha Erman Wu Jiakun Li Liang Tao Zuofeng Li Buzhou Tang Bairong Shen
The field of biomedical research has witnessed a significant increase in the
accumulation of vast amounts of textual data from various sources such as
scientific literatures, electronic health records, clinical trial reports, and
social media. However, manually processing and analyzing these extensive and
complex resources is time-consuming and inefficient. To address this challenge,
biomedical text mining, also known as biomedical natural language processing,
has garnered great attention. Community challenge evaluation competitions have
played an important role in promoting technology innovation and
interdisciplinary collaboration in biomedical text mining research. These
challenges provide platforms for researchers to develop state-of-the-art
solutions for data mining and information processing in biomedical research. In
this article, we review the recent advances in community challenges specific to
Chinese biomedical text mining. Firstly, we collect the information of these
evaluation tasks, such as data sources and task types. Secondly, we conduct
systematic summary and comparative analysis, including named entity
recognition, entity normalization, attribute extraction, relation extraction,
event extraction, text classification, text similarity, knowledge graph
construction, question answering, text generation, and large language model
evaluation. Then, we summarize the potential clinical applications of these
community challenge tasks from translational informatics perspective. Finally,
we discuss the contributions and limitations of these community challenges,
while highlighting future directions in the era of large language models.