基于中文病历数据，用大模型推荐出院用药，提升慢病管理效果。
Overview of CHIP 2025 Shared Task 2: Discharge Medication Recommendation for Metabolic Diseases Based on Chinese Electronic Health Records
- 构建高质量中文病历数据集CDrugRed，含5894条住院记录。
- 顶尖团队在测试集上达Jaccard 0.5102、F1 0.6267，验证大模型潜力。
- 适合关注医疗AI、临床决策支持的开发者和研究者。
出院用药推荐对慢性代谢性疾病患者治疗连续性、预防再入院及长期管理至关重要。本文概述了CHIP 2025共享任务2，旨在利用真实世界中国电子健康记录（EHR）数据，开发先进的自动出院用药推荐方法。为此，我们构建了包含5,894条去标识化住院记录的高质量数据集CDrugRed，覆盖3,190名患者。该任务因用药推荐的多标签特性、异构临床文本及个体化治疗方案差异而具有挑战性。共有526支队伍注册，其中167支和95支分别提交了阶段A和阶段B的有效结果。最优团队在最终测试集上取得最高性能，Jaccard得分为0.5102，F1得分为0.6267，表明基于大语言模型（LLM）的集成系统具备显著潜力。这些结果凸显了将大语言模型应用于中文EHR用药推荐的前景与现存挑战。赛后评估阶段仍开放于https://tianchi.aliyun.com/competition/entrance/532411/。
原文摘要 · Abstract (English)
Discharge medication recommendation plays a critical role in ensuring treatment continuity, preventing readmission, and improving long-term management for patients with chronic metabolic diseases. This paper present an overview of the CHIP 2025 Shared Task 2 competition, which aimed to develop state-of-the-art approaches for automatically recommending appro-priate discharge medications using real-world Chinese EHR data. For this task, we constructed CDrugRed, a high-quality dataset consisting of 5,894 de-identified hospitalization records from 3,190 patients in China. This task is challenging due to multi-label nature of medication recommendation, het-erogeneous clinical text, and patient-specific variability in treatment plans. A total of 526 teams registered, with 167 and 95 teams submitting valid results to the Phase A and Phase B leaderboards, respectively. The top-performing team achieved the highest overall performance on the final test set, with a Jaccard score of 0.5102, F1 score of 0.6267, demonstrating the potential of advanced large language model (LLM)-based ensemble systems. These re-sults highlight both the promise and remaining challenges of applying LLMs to medication recommendation in Chinese EHRs. The post-evaluation phase remains open at https://tianchi.aliyun.com/competition/entrance/532411/.
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