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.

翻译：生物医学研究领域见证了来自科学文献、电子健康记录、临床试验报告及社交媒体等多种来源的大量文本数据的显著积累。然而，手动处理和分析这些庞杂且复杂的资源耗时低效。为应对这一挑战，生物医学文本挖掘（亦称生物医学自然语言处理）获得了广泛关注。社区挑战评估竞赛在推动生物医学文本挖掘研究的技术创新与跨学科合作中发挥了重要作用。这些挑战为研究者提供了开发生物医学研究数据挖掘与信息处理最先进解决方案的平台。本文回顾了针对中文生物医学文本挖掘的社区挑战最新进展。首先，我们收集了这些评估任务的信息，包括数据来源与任务类型；其次，进行了系统性总结与比较分析，涵盖命名实体识别、实体归一化、属性抽取、关系抽取、事件抽取、文本分类、文本相似度、知识图谱构建、问答系统、文本生成及大语言模型评估；随后，从转化信息学视角总结了这些社区挑战任务的潜在临床应用；最后，探讨了这些社区挑战的贡献与局限性，并指出了大语言模型时代的未来方向。