Sohn Sunghwan, Clark Cheryl, Halgrim Scott R, Murphy Sean P, Jonnalagadda Siddhartha R, Wagholikar Kavishwar B, Wu Stephen T, Chute Christopher G, Liu Hongfang
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
Biomed Inform Insights. 2013 Jun 24;6(Suppl 1):7-16. doi: 10.4137/BII.S11634. Print 2013.
A large amount of medication information resides in the unstructured text found in electronic medical records, which requires advanced techniques to be properly mined. In clinical notes, medication information follows certain semantic patterns (eg, medication, dosage, frequency, and mode). Some medication descriptions contain additional word(s) between medication attributes. Therefore, it is essential to understand the semantic patterns as well as the patterns of the context interspersed among them (ie, context patterns) to effectively extract comprehensive medication information. In this paper we examined both semantic and context patterns, and compared those found in Mayo Clinic and i2b2 challenge data. We found that some variations exist between the institutions but the dominant patterns are common.
大量的药物信息存在于电子病历中的非结构化文本中,这需要先进的技术来进行恰当挖掘。在临床记录中,药物信息遵循特定的语义模式(例如,药物、剂量、频率和用药方式)。一些药物描述在药物属性之间包含额外的词汇。因此,了解语义模式以及穿插在其中的上下文模式(即语境模式)对于有效提取全面的药物信息至关重要。在本文中,我们研究了语义和语境模式,并比较了梅奥诊所和i2b2挑战赛数据中的模式。我们发现不同机构之间存在一些差异,但主要模式是相同的。