Mapping Chinese Medical Entities to the Unified Medical Language System  

作　　者：Luming Chen Yifan Qi Aiping Wu Lizong Deng Taijiao Jiang 

机构地区：[1]Guangzhou Laboratory,Guangzhou,China. [2]Guangzhou Medical University,Guangzhou,China. [3]Instituteof Systems Medicine,Chinese Academy of Medical Sciences&Peking Union Medical College,Beijing,China. [4]Suzhou Institute of Systems Medicine,Suzhou,China.

出　　处：《Health Data Science》2023年第1期31-40,共10页健康数据科学（英文）

基　　金：the National Key Research and Development Program of China(2021YFC2302001);the CAMS Innovation Fund for Medical Sciences(CIFMS)(2021-1-I2M-051 and 2021-I2M-1-001);the National Natural Science Foundation of China(grant 31671371);the Emergency Key Program of Guangzhou Laboratory(grant EKPG21-12)。

摘　　要：Background.Chinese medical entities have not been organized comprehensively due to the lack of welldeveloped terminology systems,which poses a challenge to processing Chinese medical texts for finegrained medical knowledge representation.To unify Chinese medical terminologies,mapping Chinese medical entities to their English counterparts in the Unified Medical Language System(UMLS)is an efficient solution.However,their mappings have not been investigated sufficiently in former research.In this study,we explore strategies for mapping Chinese medical entities to the UMLS and systematically evaluate the mapping performance.Methods.First,Chinese medical entities are translated to English using multiple web-based translation engines.Then,3 mapping strategies are investigated:(a)stringbased,(b)semantic-based,and(c)string and semantic similarity combined.In addition,cross-lingual pretrained language models are applied to map Chinese medical entities to UMLS concepts without translation.All of these strategies are evaluated on the ICD10-CN,Chinese Human Phenotype Ontology(CHPO),and RealWorld datasets.Results.The linear combination method based on the SapBERT and term frequency-inverse document frequency bag-of-words models perform the best on all evaluation datasets,with 91.85%,82.44%,and 78.43%of the top 5 accuracies on the ICD10-CN,CHPO,and RealWorld datasets,respectively.Conclusions.In our study,we explore strategies for mapping Chinese medical entities to the UMLS and identify a satisfactory linear combination method.Our investigation will facilitate Chinese medical entity normalization and inspire research that focuses on Chinese medical ontology development.

关 键 词：SEMANTIC STRING TERMINOLOGY 

分 类 号：R197.323[医药卫生—卫生事业管理] TP391.1[医药卫生—公共卫生与预防医学]