Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Community Challenges Overview and 2.1 CCKS 2. Community Challenges Overview and 2.1 CCKS 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 3. Evaluation Tasks Overview and 3.1 Information Extraction 3. Evaluation Tasks Overview and 3.1 Information Extraction 3.2 Text Classification and Text Similarity 3.2 Text Classification and Text Similarity 3.3 Knowledge Graph and Question Answering 3.3 Knowledge Graph and Question Answering 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 4. Translational Informatics in Biomedical Text Mining 4. Translational Informatics in Biomedical Text Mining 5. Discussion and Perspective 5.1. Contributions of Community Challenges 5.1. Contributions of Community Challenges 5.2. Limitations of Current Community Challenges 5.2. Limitations of Current Community Challenges 5.3. Future Perspectives in the Era of Large Language Models, and References 5.3. Future Perspectives in the Era of Large Language Models, and References Figure Legends and Tables Figure Legends and Tables 4. Translational Informatics in Biomedical Text Mining Based on the perspective of translational informatics, Figure 3 demonstrates the community challenges in biomedical text mining, which aims in health information processing that collected from diverse biomedical resources by leveraging various NLP technologies, and has numerous applications in clinical translational research. Translational informatics refers to the application of data science and computational approaches to bridge the gap between biomedical research and clinical practice. In this context, biomedical text mining plays a crucial role in extracting valuable information from large volumes of biomedical texts, such as electronic health records, scientific literatures, clinical trial reports, medical books, clinical practice guidelines, and even the internet. By using a variety of NLP technologies, including named entity recognition, normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph, question answering, and text generation, biomedical text mining enables the efficient processing and analysis of biomedical texts, leading to various clinical translational applications such as medical text mining and information processing, clinical data management, terminology standardization, health consultation, question-answering, disease prediction, epidemic forecasting, clinical decisionmaking, and knowledge discovery. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author. Authors: Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author.
