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《肝胆疾病标准数据规范》系列解读 被引量:6

Series interpretation of ‘standard data specification for liver and gallbladder diseases’
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摘要 我国是肝胆疾病大国,但在肝胆疾病大数据领域却发展滞后,目前尚无全国层面的临床及影像学数据标准,且行业内缺乏肝胆疾病的规范化标注专家共识。为解决这一问题,中华人民共和国国家卫生健康委员会联合全国肝胆专家制定首个《肝胆疾病标准数据规范》团体标准。笔者团队就标准编制背景、主要内容及建立临床意义等对《肝胆疾病标准数据规范》3项标准(标准号分别为T/CMDA 001-2020、T/CMDA 002-2020、T/CMDA 003-2020)进行解读。旨在通过解读有助于临床医学研究及人工智能医疗器械等机构对数据采集、标注、质量控制规范的理解,有利于该标准的推广应用。 Although China is a big country of hepatobiliary diseases,the development of big data in hepatobiliary diseases lags behind.At present,there is no national standard for hepatobiliary clinical and imaging data,and there is no expert consensus on standardized labeling of hepatobiliary diseases in the industry.To solve this problem,the National Health Commission organize the national hepatobiliary experts to formulate the'standard data specification for liver and gallbladder diseases'.In this paper,the authors analyze the background,main contents and clinical significance of the'standard data specification for liver and gallbladder diseases',including standards with standard No.T/CMDA 001-2020,T/CMDA 002-2020 and T/CMDA 003-2020,in order to promote the understanding of standards of data acquisition,labeling and quality control in clinical medical research and artificial intelligence medical device,and to facilitate the popularization and application of this standard.
作者 冯晓彬 黎成权 杨爱平 马兆毅 云宏旭 温晓夕 裴逸林 董家鸿 Feng Xiaobin;Li Chengquan;Yang Aiping;Ma Zhaoyi;Yun Hongxu;Wen Xiaoxi;Pei Yilin;Dong Jiahong(Department of Hepato-pancreato-biliary Surgery,Beijing Tsinghua Changgung Hospital,Institute for Precision Medicine,School of Clinical Medicine,Tsinghua University,Beijing 102218,China;National Health Commission Capacity Building and Continuing Education Center,Beijing 100191,China)
出处 《中华消化外科杂志》 CAS CSCD 北大核心 2021年第1期29-34,共6页 Chinese Journal of Digestive Surgery
基金 国家自然基金重点课题(81930119) 北京市自然基金重点课题(Z190024) 科技创新2030新一代人工智能2020年度报告(2020AAA0105005) 清华-青岛数据科学研究院(20209990017)。
关键词 肝疾病 胆道疾病 数据采集 数据标注 质量控制 标准数据集 标准规范 Liver diseases Biliary diseases Data acquisition Data labeling Quality control Standard dataset Standard specification
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