title: Enhancing clinical concept extraction with distributional semantics.
abstract: Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text. The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type "clinical trials" to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task. The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged F-score for exact match increased from 80.3% to 82.3% and the micro-averaged F-score based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the bootstrap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data.
authors.affiliation: Department of Biomedical Informatics, Arizona State University, Phoenix, AZ, USA. siddhartha@mayo.edu
authors.forename: Siddhartha
authors.initials: S
authors.lastname: Jonnalagadda
authors.forename: Trevor
authors.initials: T
authors.lastname: Cohen
authors.forename: Stephen
authors.initials: S
authors.lastname: Wu
authors.forename: Graciela
authors.initials: G
authors.lastname: Gonzalez
mesh_terms: D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical; D006801:Humans; D016239:MEDLINE; D009323:Natural Language Processing; D012660:Semantics; D017432:Unified Medical Language System
references.citation: Proc AMIA Symp. 1999;:67-71
references.pmid: 10566322
references.citation: J Am Med Inform Assoc. 2010 Nov-Dec;17(6):646-51
references.pmid: 20962126
references.citation: Proc AMIA Symp. 2001;:17-21
references.pmid: 11825149
references.citation: J Biomed Inform. 2009 Apr;42(2):390-405
references.pmid: 19232399
references.citation: Radiology. 1990 Feb;174(2):543-8
references.pmid: 2404321
references.citation: Stud Health Technol Inform. 2005;116:823-8
references.pmid: 16160360
references.citation: J Am Med Inform Assoc. 2011 Sep-Oct;18(5):552-6
references.pmid: 21685143
references.citation: IEEE Trans Neural Netw. 1995;6(3):623-41
references.pmid: 18263348
references.citation: Proc AMIA Symp. 1999;:970-4
references.pmid: 10566505
references.citation: Proc AMIA Symp. 2002;:160-4
references.pmid: 12463807
references.citation: Proc Annu Symp Comput Appl Med Care. 1995;:284-8
references.pmid: 8563286
references.citation: J Biomed Inform. 2006 Dec;39(6):589-99
references.pmid: 16359928
references.citation: Yearb Med Inform. 2008;:128-44
references.pmid: 18660887
references.citation: Pac Symp Biocomput. 2008;:652-63
references.pmid: 18229723
references.citation: AMIA Annu Symp Proc. 2003;:1057
references.pmid: 14728560
references.citation: Proc AMIA Annu Fall Symp. 1997;:595-9
references.pmid: 9357695
references.citation: BMC Bioinformatics. 2009 Jul 28;10:233
references.pmid: 19635172
references.citation: BMC Med Inform Decis Mak. 2005 Aug 31;5:30
references.pmid: 16135244
references.citation: Psychol Rev. 2007 Jan;114(1):1-37
references.pmid: 17227180
references.citation: Stud Health Technol Inform. 2010;160(Pt 1):661-5
references.pmid: 20841769
references.citation: J Am Med Inform Assoc. 2008 Jan-Feb;15(1):29-31
references.pmid: 17947620
references.citation: J Biomed Inform. 2002 Aug;35(4):222-35
references.pmid: 12755517
references.citation: AMIA Annu Symp Proc. 2003;:584-8
references.pmid: 14728240
references.citation: BMC Med Inform Decis Mak. 2006 Jul 26;6:30
references.pmid: 16872495