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首页> 外文期刊>BMC Medical Informatics and Decision Making >A computational linguistics motivated mapping of ICPC-2 PLUS to SNOMED CT
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A computational linguistics motivated mapping of ICPC-2 PLUS to SNOMED CT

机译：ICPC-2 PLUS到SNOMED CT的计算语言学驱动映射

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BackgroundA great challenge in sharing data across information systems in general practice is the lack of interoperability between different terminologies or coding schema used in the information systems. Mapping of medical vocabularies to a standardised terminology is needed to solve data interoperability problems.MethodsWe present a system to automatically map an interface terminology ICPC-2 PLUS to SNOMED CT. Three steps of mapping are proposed in this system. The UMLS metathesaurus mapping utilises explicit relationships between ICPC-2 PLUS and SNOMED CT terms in the UMLS library to perform the first stage of the mapping. Computational linguistic mapping uses natural language processing techniques and lexical similarities for the second stage of mapping between terminologies. Finally, the post-coordination mapping allows one ICPC-2 PLUS term to be mapped into an aggregation of two or more SNOMED CT terms.ResultsA total 5,971 of all 7,410 ICPC-2 terms (80.58%) were mapped to SNOMED CT using the three stages but with different levels of accuracy. UMLS mapping achieved the mapping of 53.0% ICPC2 PLUS terms to SNOMED CT with the precision rate of 96.46% and overall recall rate of 44.89%. Lexical mapping increased the result to 60.31% and post-coordination mapping gave an increase of 20.27% in mapped terms. A manual review of a part of the mapping shows that the precision of lexical mappings is around 90%. The accuracy of post-coordination has not been evaluated yet. Unmapped terms and mismatched terms are due to the differences in the structures between ICPC-2 PLUS and SNOMED CT. Terms contained in ICPC-2 PLUS but not in SNOMED CT caused a large proportion of the failures in the mappings.ConclusionMapping terminologies to a standard vocabulary is a way to facilitate consistent medical data exchange and achieve system interoperability and data standardisation. Broad scale mapping cannot be achieved by any single method and methods based on computational linguistics can be very useful for the task. Automating as much as is possible of this process turns the searching and mapping task into a validation task, which can effectively reduce the cost and increase the efficiency and accuracy of this task over manual methods.
机译：背景技术在一般实践中，跨信息系统共享数据的一个巨大挑战是信息系统中使用的不同术语或编码模式之间缺乏互操作性。解决数据互操作性问题需要将医学词汇映射到标准化术语。方法我们提出了一种将接口术语ICPC-2 PLUS自动映射到SNOMED CT的系统。在该系统中提出了三个映射步骤。 UMLS元同义词库映射利用UMLS库中ICPC-2 PLUS和SNOMED CT术语之间的显式关系来执行映射的第一阶段。计算语言映射在术语之间的映射的第二阶段使用自然语言处理技术和词汇相似性。最后，协调后映射允许将一个ICPC-2 PLUS术语映射到两个或多个SNOMED CT术语的集合中。结果使用这三个术语将总共7,410个ICPC-2术语中的5,971个（80.58％）映射到SNOMED CT阶段，但准确性不同。 UMLS映射实现了53.0％ICPC2 PLUS术语到SNOMED CT的映射，准确率为96.46％，总体召回率为44.89％。词汇映射将结果提高到60.31％，而协调后映射的映射项增加了20.27％。手动检查部分映射显示，词法映射的精度大约为90％。协调后的准确性尚未评估。未映射的术语和不匹配的术语是由于ICPC-2 PLUS和SNOMED CT之间的结构差异所致。 ICPC-2 PLUS中包含的术语而不是SNOMED CT中包含的术语造成了映射中的大部分失败。结论将术语映射到标准词汇表是促进一致的医学数据交换并实现系统互操作性和数据标准化的一种方式。大规模映射无法通过任何一种方法来实现，并且基于计算语言学的方法对于该任务可能非常有用。尽可能自动地执行此过程，将搜索和映射任务转换为验证任务，与手动方法相比，它可以有效地降低成本并提高此任务的效率和准确性。

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