# Advancing Chinese biomedical text mining with community challenges

arXiv: https://arxiv.org/abs/2403.04261 · HTML: https://mlsanity.com/paper/2403.04261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 107 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [Multimodal Deep Learning](https://mlsanity.com/paper/2301.04856/) (2023)
- [GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information](https://mlsanity.com/paper/2304.09667/) (2023)
- [CMB: A Comprehensive Medical Benchmark in Chinese](https://mlsanity.com/paper/2308.08833/) (2023)
- [CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2106.08087/) (2021)
- [BioRED: A Rich Biomedical Relation Extraction Dataset](https://mlsanity.com/paper/2204.04263/) (2022)
- [Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine](https://mlsanity.com/paper/2308.06834/) (2023)
- [MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation](https://mlsanity.com/paper/2010.07497/) (2020)
- [PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain](https://mlsanity.com/paper/2310.14151/) (2023)
- [MedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models](https://mlsanity.com/paper/2312.12806/) (2023)
- [Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID](https://mlsanity.com/paper/2104.09632/) (2021)
- [PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligence](https://mlsanity.com/paper/2307.09683/) (2023)
- [Zero-shot information extraction from radiological reports using ChatGPT](https://mlsanity.com/paper/2309.01398/) (2023)
- [Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations](https://mlsanity.com/paper/2204.09781/) (2022)
- [Overview of the PromptCBLUE Shared Task in CHIP2023](https://mlsanity.com/paper/2312.17522/) (2023)
- [Explainable AI for Bioinformatics: Methods, Tools, and Applications](https://mlsanity.com/paper/2212.13261/) (2022)
- [LitCovid in 2022: an information resource for the COVID-19 literature](https://mlsanity.com/paper/2209.13428/) (2022)
- [SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication Recommendation](https://mlsanity.com/paper/2309.05675/) (2023)
- [Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA](https://mlsanity.com/paper/2003.03875/) (2020)
- [Progress Note Understanding -- Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 Shared Task](https://mlsanity.com/paper/2303.08038/) (2023)

## Abstract (arXiv)

Objective: This study aims to review the recent advances in community challenges for biomedical text mining in China. Methods: We collected information of evaluation tasks released in community challenges of biomedical text mining, including task description, dataset description, data source, task type and related links. A systematic summary and comparative analysis were conducted on various biomedical natural language processing tasks, such as 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. Results: We identified 39 evaluation tasks from 6 community challenges that spanned from 2017 to 2023. Our analysis revealed the diverse range of evaluation task types and data sources in biomedical text mining. We explored the potential clinical applications of these community challenge tasks from a translational biomedical informatics perspective. We compared with their English counterparts, and discussed the contributions, limitations, lessons and guidelines of these community challenges, while highlighting future directions in the era of large language models. Conclusion: Community challenge evaluation competitions have played a crucial role in promoting technology innovation and fostering interdisciplinary collaboration in the field of biomedical text mining. These challenges provide valuable platforms for researchers to develop state-of-the-art solutions.