title: Classification of medication status change in clinical narratives.
abstract: The patient's medication history and status changes play essential roles in medical treatment. A notable amount of medication status information typically resides in unstructured clinical narratives that require a sophisticated approach to automated classification. In this paper, we investigated rule-based and machine learning methods of medication status change classification from clinical free text. We also examined the impact of balancing training data in machine learning classification when using the data from skewed class distribution.
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
authors.forename: Sunghwan
authors.initials: S
authors.lastname: Sohn
authors.forename: Sean P
authors.initials: SP
authors.lastname: Murphy
authors.forename: James J
authors.initials: JJ
authors.lastname: Masanz
authors.forename: Jean-Pierre A
authors.initials: JP
authors.lastname: Kocher
authors.forename: Guergana K
authors.initials: GK
authors.lastname: Savova
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D000069550:Machine Learning; D033262:Narration; D009323:Natural Language Processing