title: A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction.
abstract: OBJECTIVE
  The goal of this work was to evaluate machine learning methods, binary classification and sequence labeling, for medication-attribute linkage detection in two clinical corpora.
  
  
  METHODS
  We double annotated 3000 clinical trial announcements (CTA) and 1655 clinical notes (CN) for medication named entities and their attributes. A binary support vector machine (SVM) classification method with parsimonious feature sets, and a conditional random fields (CRF)-based multi-layered sequence labeling (MLSL) model were proposed to identify the linkages between the entities and their corresponding attributes. We evaluated the system's performance against the human-generated gold standard.
  
  
  RESULTS
  The experiments showed that the two machine learning approaches performed statistically significantly better than the baseline rule-based approach. The binary SVM classification achieved 0.94 F-measure with individual tokens as features. The SVM model trained on a parsimonious feature set achieved 0.81 F-measure for CN and 0.87 for CTA. The CRF MLSL method achieved 0.80 F-measure on both corpora.
  
  
  CONCLUSIONS
  We compared the novel MLSL method with a binary classification and a rule-based method. The MLSL method performed statistically significantly better than the rule-based method. However, the SVM-based binary classification method was statistically significantly better than the MLSL method for both the CTA and CN corpora. Using parsimonious feature sets both the SVM-based binary classification and CRF-based MLSL methods achieved high performance in detecting medication name and attribute linkages in CTA and CN.
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Qi
authors.initials: Q
authors.lastname: Li
authors.forename: Haijun
authors.initials: H
authors.lastname: Zhai
authors.forename: Louise
authors.initials: L
authors.lastname: Deleger
authors.forename: Todd
authors.initials: T
authors.lastname: Lingren
authors.forename: Megan
authors.initials: M
authors.lastname: Kaiser
authors.forename: Laura
authors.initials: L
authors.lastname: Stoutenborough
authors.forename: Imre
authors.initials: I
authors.lastname: Solti
mesh_terms: D001185:Artificial Intelligence; D002986:Clinical Trials as Topic; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008499:Medical Records; D004364:Pharmaceutical Preparations; D060388:Support Vector Machine
chemical_list: D004364:Pharmaceutical Preparations