基于混合召回和Ernie的临床术语标准化

Gongzheng Tang, Tianming Liu, Xunsheng Cai, Sida Gao, Lijun Fu
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引用次数: 0

摘要

临床术语规范化工作是医学信息统计工作不可缺少的重要组成部分。临床术语在临床实践中经常出现口语化和表达多样性的问题。因此,同样的诊断、程序、症状等有许多不同的书写方式。临床术语标准化是将非标准中文临床术语的描述文本与中文临床术语库中给定的标准词相对应。本文以中文临床文本为研究对象,采用ERNIE-health作为隐含语义评分模块,基于文本相似度检索生成候选答案,并首次在基于SNOMED CT标注的中文医学专业人员数据集上进行测试。实验表明,该方法在实际应用中取得了良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Standardization of clinical terminology based on hybrid recall and Ernie
The task of clinical terminology standardization is an indispensable part of medical information statistics works. Clinical terms often have problems with colloquial terminology and diversity of expression in clinical practice. Therefore, there are many different ways of writing the same diagnosis, procedure, symptom, etc. Clinical terminology standardization is to correspond a descriptive text of a non-standard Chinese clinical term to a given standard word in the Chinese clinical terminology database. This paper focuses on Chinese clinical texts, ERNIE-health is used as an implied semantic scoring module to generate candidate answers based on text similarity retrieval, and tested on the Chinese data set of medical professionals based on SNOMED CT annotation for the first time. Experiments show that this method has achieved good results in practical application.
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