Interoperability between phenotypes in research and healthcare terminologies--Investigating partial mappings between HPO and SNOMED CT.
Interoperability between phenotypes in research and healthcare terminologies--Investigating partial mappings between HPO and SNOMED CT.
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DOI：
10.1186/s13326-016-0047-3
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发表时间：
2016
影响因子：
1.9
通讯作者：
Bodenreider O
中科院分区：
文献类型：
--
作者：
Dhombres F;Bodenreider O
当一个术语比另一个术语粒度更细时，识别两个术语之间的部分映射特别重要，例如主要用于研究目的的人类表型本体（HPO）和主要用于医疗保健的SNOMED CT。研究并对比HPO和SNOMED CT之间推导部分映射的词汇和逻辑方法。1)词汇方法-我们识别HPO术语中的修饰符，并尝试通过UMLS将去修饰的术语映射到SNOMED CT; 2）逻辑方法-我们利用HPO中的包含关系来推断到SNOMED CT的部分映射; 3）比较-我们分析每种方法的具体贡献，并通过人工审查评估部分映射的质量。有7358个HPO概念没有完全映射到SNOMED CT。我们确定部分映射的词汇为33%，逻辑为82%。我们确定部分映射的词汇和逻辑为27%。部分映射的临床相关性（对于群组选择用例）对于词汇映射为49%，对于逻辑映射为67%。通过完全和部分映射，10，454个HPO概念中的92%可以映射到SNOMED CT（30%完全和62%部分）。HPO和SNOMED CT之间的等效映射允许使用这两个系统描述的数据之间的互操作性。但是，由于焦点和粒度的差异，等价性只可能用于30%的HPO类。在其余情况下，部分映射提供了在两个系统之间穿越的次佳方法。词法映射和逻辑映射技术都能产生另一种技术无法产生的映射，这表明这两种技术是互补的。最后，这项工作证明了有趣的属性（词汇和逻辑）的HPO和SNOMED CT，并说明了一些限制映射通过UMLS。
Identifying partial mappings between two terminologies is of special importance when one terminology is finer-grained than the other, as is the case for the Human Phenotype Ontology (HPO), mainly used for research purposes, and SNOMED CT, mainly used in healthcare. To investigate and contrast lexical and logical approaches to deriving partial mappings between HPO and SNOMED CT. 1) Lexical approach—We identify modifiers in HPO terms and attempt to map demodified terms to SNOMED CT through UMLS; 2) Logical approach—We leverage subsumption relations in HPO to infer partial mappings to SNOMED CT; 3) Comparison—We analyze the specific contribution of each approach and evaluate the quality of the partial mappings through manual review. There are 7358 HPO concepts with no complete mapping to SNOMED CT. We identified partial mappings lexically for 33 % of them and logically for 82 %. We identified partial mappings both lexically and logically for 27 %. The clinical relevance of the partial mappings (for a cohort selection use case) is 49 % for lexical mappings and 67 % for logical mappings. Through complete and partial mappings, 92 % of the 10,454 HPO concepts can be mapped to SNOMED CT (30 % complete and 62 % partial). Equivalence mappings between HPO and SNOMED CT allow for interoperability between data described using these two systems. However, due to differences in focus and granularity, equivalence is only possible for 30 % of HPO classes. In the remaining cases, partial mappings provide a next-best approach for traversing between the two systems. Both lexical and logical mapping techniques produce mappings that cannot be generated by the other technique, suggesting that the two techniques are complementary to each other. Finally, this work demonstrates interesting properties (both lexical and logical) of HPO and SNOMED CT and illustrates some limitations of mapping through UMLS.