糖尿病电子病历实体及关系标注语料库构建(Construction of Corpus for Entity and Relation Annotation of Diabetes Electronic Medical Records)

no code implementations • CCL 2021 • Yajuan Ye, Bin Hu, Kunli Zhang, Hongying Zan

“电子病历是医疗信息的重要来源, 包含大量与医疗相关的领域知识。本文从糖尿病电子病历文本入手, 在调研了国内外已有的电子病历语料库的基础上, 参考坉圲坂圲实体及关系分类, 建立了糖尿病电子病历实体及实体关系分类体系, 并制定了标注规范。利用实体及关系标注平台, 进行了实体及关系预标注及多轮人工校对工作, 形成了糖尿病电子病历实体及关系标注语料库(Diabetes Electronic Medical Record entity and Related Corpus DEMRC)。所构建的DEMRC包含8899个实体、456个实体修饰及16564个关系。对DEMRC进行一致性评价和分析, 标注结果达到了较高的一致性。针对实体识别和实体关系抽取任务, 分别采用基于迁移学习的Bi-LSTM-CRF模型和RoBERTa模型进行初步实验, 并对语料库中的各类实体及关系进行评估, 为后续糖尿病电子病历实体识别及关系抽取研究以及糖尿病知识图谱构建打下基础。”

脑卒中疾病电子病历实体及实体关系标注语料库构建(Corpus Construction for Named-Entity and Entity Relations for Electronic Medical Records of Stroke Disease)

no code implementations • CCL 2021 • Hongyang Chang, Hongying Zan, Yutuan Ma, Kunli Zhang

“本文探讨了在脑卒中疾病中文电子病历文本中实体及实体间关系的标注问题, 提出了适用于脑卒中疾病电子病历文本的实体及实体关系标注体系和规范。在标注体系和规范的指导下, 进行了多轮的人工标注及校正工作, 完成了158万余字的脑卒中电子病历文本实体及实体关系的标注工作。构建了脑卒中电子病历实体及实体关系标注语料库(Stroke Electronic Medical Record entity and entity related Corpus SEMRC)。所构建的语料库共包含命名实体10594个, 实体关系14457个。实体名标注一致率达到85. 16%, 实体关系标注一致率达到94. 16%。”

面向医学文本处理的医学实体标注规范(Medical Entity Annotation Standard for Medical Text Processing)

no code implementations • CCL 2020 • huan zhang, Yuan Zong, Baobao Chang, Zhifang Sui, Hongying Zan, Kunli Zhang

Konwledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph

no code implementations • CCL 2020 • Kunli Zhang, Xu Zhao, Lei Zhuang, Qi Xie, Hongying Zan

In this paper, we treat the diagnosis assistant as a multi-label classification task and propose a Knowledge-Enabled Diagnosis Assistant (KEDA) model for the obstetric diagnosis assistant.

Disease Prediction Multi-Label Classification

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

2 code implementations • ACL 2022 • Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei LI, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.

Intent Classification Medical Concept Normalization +8