title: Analysis of cross-institutional medication description patterns in clinical narratives.
abstract: 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.
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
authors.forename: Sunghwan
authors.initials: S
authors.lastname: Sohn
authors.forename: Cheryl
authors.initials: C
authors.lastname: Clark
authors.forename: Scott R
authors.initials: SR
authors.lastname: Halgrim
authors.forename: Sean P
authors.initials: SP
authors.lastname: Murphy
authors.forename: Siddhartha R
authors.initials: SR
authors.lastname: Jonnalagadda
authors.forename: Kavishwar B
authors.initials: KB
authors.lastname: Wagholikar
authors.forename: Stephen T
authors.initials: ST
authors.lastname: Wu
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
authors.forename: Hongfang
authors.initials: H
authors.lastname: Liu
keywords: electronic medical record; medication extraction; natural language processing