title: High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge.
abstract: OBJECTIVE
  Medication information comprises a most valuable source of data in clinical records. This paper describes use of a cascade of machine learners that automatically extract medication information from clinical records.
  
  
  METHODS
  Authors developed a novel supervised learning model that incorporates two machine learning algorithms and several rule-based engines.
  
  
  METHODS
  Evaluation of each step included precision, recall and F-measure metrics. The final outputs of the system were scored using the i2b2 workshop evaluation metrics, including strict and relaxed matching with a gold standard.
  
  
  RESULTS
  Evaluation results showed greater than 90% accuracy on five out of seven entities in the name entity recognition task, and an F-measure greater than 95% on the relationship classification task. The strict micro averaged F-measure for the system output achieved best submitted performance of the competition, at 85.65%.
  
  
  CONCLUSIONS
  Clinical staff will only use practical processing systems if they have confidence in their reliability. Authors estimate that an acceptable accuracy for a such a working system should be approximately 95%. This leaves a significant performance gap of 5 to 10% from the current processing capabilities.
  
  
  CONCLUSIONS
  A multistage method with mixed computational strategies using a combination of rule-based classifiers and statistical classifiers seems to provide a near-optimal strategy for automated extraction of medication information from clinical records.
authors.affiliation: Faculty of Engineering and IT, the University of Sydney, Sydney, Australia. jonpat@it.usyd.edu.au
authors.forename: Jon
authors.initials: J
authors.lastname: Patrick
authors.forename: Min
authors.initials: M
authors.lastname: Li
mesh_terms: D001185:Artificial Intelligence; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D004364:Pharmaceutical Preparations
chemical_list: D004364:Pharmaceutical Preparations
references.citation: Pac Symp Biocomput. 2005;:308-18
references.pmid: 15759636
references.citation: AMIA Annu Symp Proc. 2008 Nov 06;:237-41
references.pmid: 18999147
references.citation: J Am Med Inform Assoc. 2010 Sep-Oct;17(5):514-8
references.pmid: 20819854
references.citation: Proc AMIA Annu Fall Symp. 1996;:388-92
references.pmid: 8947694