DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx
نویسندگان
چکیده
منابع مشابه
Extending NegEx with Kernel Methods for Negation Detection in Clinical Text
NegEx is a popular rule-based system used to identify negated concepts in clinical notes. This system has been reported to perform very well by numerous studies in the past. In this paper, we demonstrate the use of kernel methods to extend the performance of NegEx. A kernel leveraging the rules of NegEx and its output as features, performs as well as the rule-based system. An improvement in per...
متن کاملNegation detection in Swedish clinical text: An adaption of NegEx to Swedish
BACKGROUND Most methods for negation detection in clinical text have been developed for English text, and there is a need for evaluating the feasibility of adapting these methods to other languages. A Swedish adaption of the English rule-based negation detection system NegEx, which detects negations through the use of trigger phrases, was therefore evaluated. RESULTS The Swedish adaption of N...
متن کاملNegation Scope Delimitation in Clinical Text Using Three Approaches: NegEx; PyConTextNLP and SynNeg
Negation detection is a key component in clinical information extraction systems, as health record text contains reasonings in which the physician excludes different diagnoses by negating them. Many systems for negation detection rely on negation cues (e.g. not), but only few studies have investigated if the syntactic structure of the sentences can be used for determining the scope of these cue...
متن کاملNegation Detection in Swedish Clinical Text
NegEx, a rule-based algorithm that detects negations in English clinical text, was translated into Swedish and evaluated on clinical text written in Swedish. The NegEx algorithm detects negations through the use of trigger phrases, which indicate that a preceding or following concept is negated. A list of English trigger phrases was translated into Swedish, taking grammatical differences betwee...
متن کاملDependency Parser-based Negation Detection in Clinical Narratives
Negation of clinical named entities is common in clinical documents and is a crucial factor to accurately compile patients' clinical conditions and to further support complex phenotype detection. In 2009, Mayo Clinic released the clinical Text Analysis and Knowledge Extraction System (cTAKES), which includes a negation annotator that identifies negation status of a named entity by searching for...
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ژورنال
عنوان ژورنال: Journal of Biomedical Informatics
سال: 2015
ISSN: 1532-0464
DOI: 10.1016/j.jbi.2015.02.010