title: Extracting medication information from clinical text.
abstract: The Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records focused on the identification of medications, their dosages, modes (routes) of administration, frequencies, durations, and reasons for administration in discharge summaries. This challenge is referred to as the medication challenge. For the medication challenge, i2b2 released detailed annotation guidelines along with a set of annotated discharge summaries. Twenty teams representing 23 organizations and nine countries participated in the medication challenge. The teams produced rule-based, machine learning, and hybrid systems targeted to the task. Although rule-based systems dominated the top 10, the best performing system was a hybrid. Of all medication-related fields, durations and reasons were the most difficult for all systems to detect. While medications themselves were identified with better than 0.75 F-measure by all of the top 10 systems, the best F-measure for durations and reasons were 0.525 and 0.459, respectively. State-of-the-art natural language processing systems go a long way toward extracting medication names, dosages, modes, and frequencies. However, they are limited in recognizing duration and reason fields and would benefit from future research.
authors.affiliation: Department of Information Studies, University at Albany, State University of New York, Albany, NY, USA. ouzuner@albany.edu
authors.forename: Ozlem
authors.initials: O
authors.lastname: Uzuner
authors.forename: Imre
authors.initials: I
authors.lastname: Solti
authors.forename: Eithon
authors.initials: E
authors.lastname: Cadag
mesh_terms: D003203:Computers, Hybrid; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D010352:Patient Dropouts; D004364:Pharmaceutical Preparations
chemical_list: D004364:Pharmaceutical Preparations
references.citation: J Am Med Inform Assoc. 2010 Sep-Oct;17(5):519-23
references.pmid: 20819855
references.citation: AMIA Annu Symp Proc. 2008 Nov 06;:237-41
references.pmid: 18999147
references.citation: AMIA Annu Symp Proc. 2008 Nov 06;:66-70
references.pmid: 18999079
references.citation: Pac Symp Biocomput. 2005;:308-18
references.pmid: 15759636
references.citation: J Am Med Inform Assoc. 2009 Jul-Aug;16(4):561-70
references.pmid: 19390096
references.citation: Stud Health Technol Inform. 2004;107(Pt 2):773-7
references.pmid: 15360917
references.citation: J Am Med Inform Assoc. 2008 Jan-Feb;15(1):14-24
references.pmid: 17947624
references.citation: J Am Med Inform Assoc. 2007 Sep-Oct;14(5):550-63
references.pmid: 17600094
references.citation: Proc AMIA Annu Fall Symp. 1996;:388-92
references.pmid: 8947694
references.citation: J Am Med Inform Assoc. 2009 May-Jun;16(3):362-70
references.pmid: 19261947
references.citation: AMIA Annu Symp Proc. 2007 Oct 11;:438-42
references.pmid: 18693874
references.citation: BMC Bioinformatics. 2005;6 Suppl 1:S1
references.pmid: 15960821
references.citation: Int J Med Inform. 2009 Apr;78(4):284-91
references.pmid: 18838293