"中文电子病历国际疾病分类(ICD)诊断编码评测依托第二十四届中国计算语言学大会(CCL)举办。该评测聚焦于自然语言处理技术在智能医疗领域的应用,旨在从真实脱敏的电子病历文本中自动分析关键临床表征,实现主诊断及其他诊断ICD编码的精准预测与分配,从而辅助临床医生与专业编码员提升编码工作的准确性和效率。本次评测在阿里云天池平台进行,获得了学术界与工业界的广泛关注和积极参与。数据显示,共有445支队伍报名参赛,其中A榜和B榜分别有85支和36支队伍成功提交了有效结果。最终,8支表现优异的队伍受邀撰写并分享了其技术报告,为推动该领域的技术进步与方法创新贡献了宝贵经验。本次评测的详细信息可参见相关发布页面。"

"中医辨证辨病及中药处方生成评测任务专注于中医“辨证论治”。该任务由齐鲁工业大学(山东省科学院)与山东中医药大学附属医院联合发起,基于真实病历构建了中医“辨证论治”全流程公开数据集TCM-TBOSD,覆盖10类中医证型、4类中医疾病及381种常见中药。评测任务设立两个子任务:中医多标签辨证辨病与中药处方推荐,旨在系统评估大模型在中医诊疗全过程中的建模与推理能力。本次评测收到了学术界与产业界的广泛关注,评测共吸引123支队伍参与,35支队伍晋级复赛,最终提交了8份高质量技术报告。评测结果表明,大语言模型在中医任务中展现出良好的适应性与发展潜力,为中医智能化提供了可行路径与技术参考。详细信息可以从网址查看我们的评测任务。"

Medical entity disambiguation (MED) plays a crucial role in natural language processing and biomedical domains, which is the task of mapping ambiguous medical mentions to structured candidate medical entities from knowledge bases (KBs). However, existing methods for MED often fail to fully utilize the knowledge within medical KBs and overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguation performance. To address these limitations, this paper proposes a novel approach, MED with Medical Mention Relation and Fine-grained Entity Knowledge (MMR-FEK). Specifically, MMR-FEK incorporates a mention relation fusion module and an entity knowledge fusion module, followed by an interaction module. The former employs a relation graph convolutional network to fuse mention relation information between medical mentions to enhance mention representations, while the latter leverages an attention mechanism to fuse synonym and type information of candidate entities to enhance entity representations. Afterwards, an interaction module is designed to employ a bidirectional attention mechanism to capture interactions between mentions and entities to generate the matching representation. Extensive experiments on two publicly available real-world datasets demonstrate MMR-FEK’s superiority over state-of-the-art(SOTA) MED baselines across all metrics. Our source code is publicly available.