Geisinger Health Systems, Danville, PA, USA.
BMC Med Inform Decis Mak. 2010 Oct 28;10:66. doi: 10.1186/1472-6947-10-66.
The Systematic Nomenclature of Medicine Clinical Terms (SNOMED CT) is being advocated as the foundation for encoding clinical documentation. While the electronic medical record is likely to play a critical role in pharmacovigilance - the detection of adverse events due to medications - classification and reporting of Adverse Events is currently based on the Medical Dictionary of Regulatory Activities (MedDRA). Complete and high-quality MedDRA-to-SNOMED CT mappings can therefore facilitate pharmacovigilance. The existing mappings, as determined through the Unified Medical Language System (UMLS), are partial, and record only one-to-one correspondences even though SNOMED CT can be used compositionally. Efforts to map previously unmapped MedDRA concepts would be most productive if focused on concepts that occur frequently in actual adverse event data. We aimed to identify aspects of MedDRA that complicate mapping to SNOMED CT, determine pattern in unmapped high-frequency MedDRA concepts, and to identify types of integration errors in the mapping of MedDRA to UMLS.
Using one years' data from the US Federal Drug Administrations Adverse Event Reporting System, we identified MedDRA preferred terms that collectively accounted for 95% of both Adverse Events and Therapeutic Indications records. After eliminating those already mapping to SNOMED CT, we attempted to map the remaining 645 Adverse-Event and 141 Therapeutic-Indications preferred terms with software assistance.
All but 46 Adverse-Event and 7 Therapeutic-Indications preferred terms could be composed using SNOMED CT concepts: none of these required more than 3 SNOMED CT concepts to compose. We describe the common composition patterns in the paper. About 30% of both Adverse-Event and Therapeutic-Indications Preferred Terms corresponded to single SNOMED CT concepts: the correspondence was detectable by human inspection but had been missed during the integration process, which had created duplicated concepts in UMLS.
Identification of composite mapping patterns, and the types of errors that occur in the MedDRA content within UMLS, can focus larger-scale efforts on improving the quality of such mappings, which may assist in the creation of an adverse-events ontology.
系统命名法医学术语（SNOMED CT）被提倡作为编码临床文档的基础。虽然电子病历可能在药物警戒中发挥关键作用-检测由于药物引起的不良事件-但不良事件的分类和报告目前基于监管活动医学词典（MedDRA）。因此，完整且高质量的 MedDRA 到 SNOMED CT 映射可以促进药物警戒。通过统一医学语言系统（UMLS）确定的现有映射是不完整的，仅记录一对一的对应关系，尽管 SNOMED CT 可以组合使用。如果集中精力于实际不良事件数据中经常出现的概念，那么对以前未映射的 MedDRA 概念进行映射的工作将最有成效。我们的目标是确定使 MedDRA 映射到 SNOMED CT 变得复杂的方面，确定未映射高频 MedDRA 概念的模式，并确定 MedDRA 到 UMLS 映射中的集成错误类型。
使用美国联邦药物管理局不良事件报告系统一年的数据，我们确定了 MedDRA 首选术语，这些术语共同占不良事件和治疗指示记录的 95%。在消除已经映射到 SNOMED CT 的术语后，我们尝试使用软件辅助映射其余的 645 个不良事件和 141 个治疗指示首选术语。
除了 46 个不良事件和 7 个治疗指示首选术语外，所有术语都可以使用 SNOMED CT 概念组成：这些术语都不需要超过 3 个 SNOMED CT 概念来组成。我们在本文中描述了常见的组成模式。约 30%的不良事件和治疗指示首选术语对应于单个 SNOMED CT 概念：通过人工检查可以检测到这种对应关系，但在集成过程中被忽略了，这在 UMLS 中创建了重复的概念。
识别复合映射模式以及在 UMLS 中 MedDRA 内容中发生的错误类型，可以将更大规模的努力集中在提高此类映射的质量上，这可能有助于创建不良事件本体。
