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arXiv Computation and Language By Xuanlyu Zhu, Feng Tian, Yang Zhong, Pengqi Zheng, Xiwen Bi, Ling Luo, Yuanyuan Sun, Jian Wang, Lingfei Lin

Overview of CHIP 2026 Shared Task 1: Patient-Specific Phenotype Concept Recognition in Full-Text Biomedical Literature

Read the original on arXiv Computation and Language →

The paper introduces CHIP 2026 Shared Task 1, which focuses on patient‑specific phenotype concept recognition in full‑text biomedical literature. The task is split into two subtasks—full‑text phenotype concept recognition and patient‑specific phenotype association—using 200 annotated articles from the PatientPheX dataset that include 506 patients, 15,193 phenotype mentions, and 5,656 patient‑phenotype associations. A large community of 455 teams participated, with the best system scoring 0.7389, surpassing the baseline by 8.37 percentage points, and demonstrating the effectiveness of biomedical language models, large language models, and ensemble strategies while highlighting ongoing challenges in patient attribution and phenotype normalization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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