DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx

Mehrabi, Saeed; Krishnan, Anand; Sohn, Sunghwan; Roch, Alexandra M.; Schmidt, Heidi; Kesterson, Joe; Beesley, Chris; Dexter, Paul; Schmidt, C. Max; Liu, Hongfang; Palakal, Mathew

doi:10.1016/j.jbi.2015.02.010

Cited by 109 publications

(74 citation statements)

References 27 publications

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“…29 Further, the limited size (and domain) of the CRAFT corpus means that the estimates of precision and recall will not perfectly reflect the changes in performance over the entire PMCOA corpus. 30 However, we believe that some of the changes that showed little effect in CRAFT are actually useful over the entire PMCOA corpus, suggested by the fact that the new top 20 concepts for each ontology have more realistic concept frequencies.…”

Section: Discussionmentioning

confidence: 99%

Improving precision in concept normalization

et al. 2017

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Most natural language processing applications exhibit a trade-off between precision and recall. In some use cases for natural language processing, there are reasons to prefer to tilt that trade-off toward high precision. Relying on the Zipfian distribution of false positive results, we describe a strategy for increasing precision, using a variety of both pre-processing and post-processing methods. They draw on both knowledge-based and frequentist approaches to modeling language. Based on an existing high-performance biomedical concept recognition pipeline and a previously published manually annotated corpus, we apply this hybrid rationalist/empiricist strategy to concept normalization for eight different ontologies. Which approaches did and did not improve precision varied widely between the ontologies.

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Section: Discussionmentioning

confidence: 99%

Improving precision in concept normalization

et al. 2017

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“…There are different versions of NegEx (South et al, 2007;Harkema et al, 2009), and it has been adapted to Swedish (Skeppstedt, 2011), French (Deléger and Grouin, 2012), Dutch (Afzal et al, 2014), and Spanish (Costumero et al, 2014). In addition, the systems developed by Sohn et al (2012) (DepNeg) and Mehrabi et al (2015) (DEEPEN) are based on or use NegEx complemented with a dependency-based parser to improve scope detection. And, in another line of research, Goldin and Chapman (2003) use Naive Bayes and Decision Trees to increase the NegEx's precision of negation with only the word "not".…”

Section: Related Workmentioning

confidence: 99%

Annotation of negation in the IULA Spanish Clinical Record Corpus

Marimon¹,

Vivaldi²,

Bel³

2017

Proceedings of the Workshop Computational Semantics Beyond Events and Roles

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This paper presents the IULA Spanish Clinical Record Corpus, a corpus of 3,194 sentences extracted from anonymized clinical records and manually annotated with negation markers and their scope. The corpus was conceived as a resource to support clinical text-mining systems, but it is also a useful resource for other Natural Language Processing systems handling clinical texts: automatic encoding of clinical records, diagnosis support, term extraction, among others, as well as for the study of clinical texts. The corpus is publicly available with a CC-BY-SA 3.0 license.

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“…Different tools exist that can detect some types of negation in text, such as NegEx [34], DEEPEN [35], MedLEE [36], ConText [37] and ontology-based-approaches [38]. However, these are not general-purpose parsers and mostly focus on negation of specific medical terms and conditions and often in very limited domains.…”

Section: Identification Of Negation Using Natural Language Processingmentioning

confidence: 99%

NegAIT: A new parser for medical text simplification using morphological, sentential and double negation

Mukherjee

Leroy

Kauchak

et al. 2017

Journal of Biomedical Informatics

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Many different text features influence text readability and content comprehension. Negation is commonly suggested as one such feature, but few general-purpose tools exist to discover negation and studies of the impact of negation on text readability are rare. In this paper, we introduce a new negation parser (NegAIT) for detecting morphological, sentential, and double negation. We evaluated the parser using a human annotated gold standard containing 500 Wikipedia sentences and achieved 95%, 89% and 67% precision with 100%, 80%, and 67% recall, respectively. We also investigate two applications of this new negation parser. First, we performed a corpus statistics study to demonstrate different negation usage in easy and difficult text. Negation usage was compared in six corpora: patient blogs (4 K sentences), Cochrane reviews (91 K sentences), PubMed abstracts (20 K sentences), clinical trial texts (48 K sentences), and English and Simple English Wikipedia articles for different medical topics (60 K and 6 K sentences). The most difficult text contained the least negation. However, when comparing negation types, difficult texts (i.e., Cochrane, PubMed, English Wikipedia and clinical trials) contained significantly (p < 0.01) more morphological negations. Second, we conducted a predictive analytics study to show the importance of negation in distinguishing between easy and difficulty text. Five binary classifiers (Naïve Bayes, SVM, decision tree, logistic regression and linear regression) were trained using only negation information. All classifiers achieved better performance than the majority baseline. The Naïve Bayes’ classifier achieved the highest accuracy at 77% (9% higher than the majority baseline).

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DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx

Cited by 109 publications

References 27 publications

Improving precision in concept normalization

Improving precision in concept normalization

Annotation of negation in the IULA Spanish Clinical Record Corpus

NegAIT: A new parser for medical text simplification using morphological, sentential and double negation

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