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 and material 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.

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“…In addition, some research compares or combines clinical texts with other types of texts [10,12,32]. Also of note, some research addresses a wide range of languages other than English, including Chinese [17,18], Dutch [20], Finnish [13,29], French [4], and Swedish [10,16,21].…”

“…For instance, Sohn et al address entity normalization for medications by automatically mapping entities into the concept unique identifiers of RxNorm [15]. Other work also addresses the tasks of negation [19] and context [20][21] detection, which are found to be difficult to generalize across languages or even datasets in the same language. Within-sentence analysis can then be used to perform sentence classification in online health communities to detect the presence of adverse drug reactions [32] or to categorize a sentence as conveying emotional or informational support to other users [23].…”

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“…However, they require language-specific rules and lexicons. pyConTextNLP [51], a rule-based system for classifying assertions (negation and/or uncertainty modifiers) of disease mentions, was ported from English to Swedish [52]. The system relies on a cue lexicon with scoping rules.…”

Detecting speculations, contrasts and conditionals in consumer reviews

“…ing speculations in clinical texts (Velupillai et al, 2014), and for contrast from constructions listed by Reese et al (2007). These seeds were then expanded with neighbours in a distributional semantics space (Gavagai, 2015) and from a traditional synonym lexicon (Oxford University Press, 2013).…”