计算机科学 ›› 2021, Vol. 48 ›› Issue (11): 287-293.doi: 10.11896/jsjkx.201200016
余杰1, 纪斌1, 刘磊2, 李莎莎1, 马俊1, 刘慧君1
YU Jie1, JI Bin1, LIU Lei2, LI Sha-sha1, MA Jun1, LIU Hui-jun1
摘要: 临床病历电子化的推广普及使得利用自动化的方法从病历中快速抽取高价值的信息成为可能。作为一种重要的医学信息,肿瘤医疗事件由描述恶性肿瘤的一系列属性构成。近年来,肿瘤医疗事件抽取已成为学术界的一个研究热点,众多学术会议将其发布为评测任务,并提供了一系列高质量的标注数据。针对肿瘤医疗事件属性离散的特点,文中提出了一种中文医疗事件的联合抽取方法,实现了肿瘤原发部位和原发肿瘤大小两种属性的联合抽取和肿瘤转移部位的抽取。此外,针对肿瘤医疗事件标注文本的数量和类型少的问题,提出了一种基于关键信息全域随机替换的伪数据生成算法,提升了联合抽取方法对不同类型肿瘤医疗事件抽取的迁移学习能力。所提方法获得了CCKS2020中文电子病历临床医疗事件抽取评测任务的第三名,在CCKS2019和CCKS2020数据集上的大量实验验证了所提方法的有效性。
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