arXiv cs.CL· Xuanlyu Zhu, Feng Tian, Yang Zhong, Pengqi Zheng, Xiwen Bi, Ling Luo, Yuanyuan Sun, Jian Wang, Lingfei Lin·· 13 小时前AI 评分22
CHIP 2026 共享任务 1 综述：全文生物医学文献中的患者特异性表型概念识别
Overview of CHIP 2026 Shared Task 1: Patient-Specific Phenotype Concept Recognition in Full-Text Biomedical Literature
AI 导读
CHIP 2026 共享任务 1 聚焦全文生物医学文献中的患者特异性表型概念识别，设全文表型概念识别与患者-表型关联两个子任务。评测基于 PatientPheX 数据集的 200 篇标注全文文献，含 506 名患者、15,193 个表型提及和 5,656 条患者-表型关联，共 455 支队伍注册，Phase A 和 Phase B 分别收到 189 与 103 份有效提交。
正文 · 原文
Abstract:Automatic phenotype information extraction plays an important role in rare disease diagnosis, genotype-phenotype analysis, and precision medicine. However, accurately identifying phenotype concepts and determining their associations with specific patients remain challenging, particularly in full-text biomedical articles involving multiple patients and complex clinical descriptions. This paper presents an overview of CHIP 2026 Shared Task 1, which aims to promote the development of methods for patient-specific phenotype concept recognition from full-text biomedical literature. The task consists of two subtasks: full-text phenotype concept recognition and patient-specific phenotype association. For this evaluation, we used 200 annotated full-text biomedical articles from the PatientPheX dataset, containing 506 patients, 15,193 phenotype mentions, and 5,656 patient-phenotype associations. A total of 455 teams registered for the competition, with 189 and 103 valid submissions received on the Phase A and Phase B leaderboards, respectively. The best-performing system achieved an overall score of 0.7389 on the final test set, outperforming the baseline by 8.37 percentage points. The top-performing teams explored diverse approaches combining biomedical language models, large language models (LLMs), and ensemble strategies. The evaluation results highlight both the potential of these approaches and the remaining challenges in patient attribution and phenotype normalization from full-text biomedical literature. The post-evaluation phase remains open at this https URL.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.29606 [cs.CL] |
| (or arXiv:2610.29606v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29606 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ling Luo [view email]
[v1]
Wed, 21 Oct 2026 11:45:35 UTC (988 KB)
来源：arXiv cs.CL · arxiv.org