title: Cue-based assertion classification for Swedish clinical text--developing a lexicon for pyConTextSwe.
abstract: OBJECTIVE
  The ability of a cue-based system to accurately assert whether a disorder is affirmed, negated, or uncertain is dependent, in part, on its cue lexicon. In this paper, we continue our study of porting an assertion system (pyConTextNLP) from English to Swedish (pyConTextSwe) by creating an optimized assertion lexicon for clinical Swedish.
  
  
  METHODS
  We integrated cues from four external lexicons, along with generated inflections and combinations. We used subsets of a clinical corpus in Swedish. We applied four assertion classes (definite existence, probable existence, probable negated existence and definite negated existence) and two binary classes (existence yes/no and uncertainty yes/no) to pyConTextSwe. We compared pyConTextSwe's performance with and without the added cues on a development set, and improved the lexicon further after an error analysis. On a separate evaluation set, we calculated the system's final performance.
  
  
  RESULTS
  Following integration steps, we added 454 cues to pyConTextSwe. The optimized lexicon developed after an error analysis resulted in statistically significant improvements on the development set (83% F-score, overall). The system's final F-scores on an evaluation set were 81% (overall). For the individual assertion classes, F-score results were 88% (definite existence), 81% (probable existence), 55% (probable negated existence), and 63% (definite negated existence). For the binary classifications existence yes/no and uncertainty yes/no, final system performance was 97%/87% and 78%/86% F-score, respectively.
  
  
  CONCLUSIONS
  We have successfully ported pyConTextNLP to Swedish (pyConTextSwe). We have created an extensive and useful assertion lexicon for Swedish clinical text, which could form a valuable resource for similar studies, and which is publicly available.
authors.affiliation: Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, 164 40 Kista, Sweden. Electronic address: sumithra@dsv.su.se.
authors.forename: Sumithra
authors.initials: S
authors.lastname: Velupillai
authors.affiliation: Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, 164 40 Kista, Sweden. Electronic address: mariask@dsv.su.se.
authors.forename: Maria
authors.initials: M
authors.lastname: Skeppstedt
authors.affiliation: Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, 164 40 Kista, Sweden; Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Widerström Building, Tomtebodavägen 18A, Solna, Sweden. Electronic address: maria.kvist@karolinska.se.
authors.forename: Maria
authors.initials: M
authors.lastname: Kvist
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, BAUM 423, Pittsburgh, PA 15206-3701, United States. Electronic address: dlm31@pitt.edu.
authors.forename: Danielle
authors.initials: D
authors.lastname: Mowery
authors.affiliation: Department of Radiology, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, United States. Electronic address: brian.chapman@utah.edu.
authors.forename: Brian E
authors.initials: BE
authors.lastname: Chapman
authors.affiliation: Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, 164 40 Kista, Sweden. Electronic address: hercules@dsv.su.se.
authors.forename: Hercules
authors.initials: H
authors.lastname: Dalianis
authors.affiliation: Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112-5775, United States. Electronic address: wendy.chapman@utah.edu.
authors.forename: Wendy W
authors.initials: WW
authors.lastname: Chapman
mesh_terms: D001185:Artificial Intelligence; D003463:Cues; D057286:Electronic Health Records; D006801:Humans; D007802:Language; D009323:Natural Language Processing; D012660:Semantics; D013548:Sweden; D014177:Translations; D035501:Uncertainty; D018875:Vocabulary, Controlled
keywords: Assertion classification; Clinical text mining; Dictionaries; Electronic health records; Information extraction; Medical Language Processing