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Interoperability of Medication Classification Systems: Lessons Learned Mapping Established Pharmacologic Classes (EPCs) to SNOMED CT

机译：药物分类系统的互操作性：经验教训映射建立的药理学课程（EPCS）令人愤怒的CT

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Interoperability among medication classification systems is known to be limited. We investigated the mapping of the Established Pharmacologic Classes (EPCs) to SNOMED CT. We compared lexical and instance-based methods to an expert-reviewed reference standard to evaluate contributions of these methods. Of the 543 EPCs, 284 had cm equivalent SNOMED CT class, 205 were more specific, and 54 could not be mapped. Precision, recall, and F1 score were 0.416, 0.620, and 0.498 for lexical mapping and 0.616, 0.504, and 0.554 for instance-based mapping. Each automatic method has strengths, weaknesses, and unique contributions in mapping between medication classification systems. In our experience, it was beneficial to consider the mapping provided by both automated methods for identifying potential matches, gaps, inconsistencies, and opportunities for quality improvement between classifications. However, manual review by subject matter experts is still needed to select the most relevant mappings.
机译：已知药物分类系统之间的互操作性受到限制。我们调查了已建立的药理学课程（EPC）的映射到Snomed CT。我们将基于词汇和实例的方法与专家审查的参考标准进行了比较，以评估这些方法的贡献。在543个EPC中，284个具有CM当量的Snomed CT类，205个更具体，54个无法映射。精确，召回和F1分数为110.416,0.620和0.498，用于基于型号的0.616,0.504和0.554。每个自动方法都具有在药物分类系统之间的映射方面具有优势，劣势和独特的贡献。在我们的经验中，考虑通过自动化方法提供的映射来确定潜在的匹配，差距，不一致和分类之间质量改进的机会。但是，仍然需要由主题专家进行手动审查来选择最相关的映射。

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