Medical Entity Recognition and Negation Extraction: Assessment of NegEx on Health Records in Spanish
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This work focuses on biomedical text mining. The core of this work is to make a step ahead in the negation detection of biomedical entities on Electronic Health Records (EHRs), where the detection of non-negated entities is as important as the identification of negated entities. For instance, the identification of a negated entity as factual, can produce diagnostic errors in decision support systems.
Negated entity recognition tackles two tasks: (1) entity recognition; (2) entity classification as negated or not. To identify negations, in the literature rule-based and machine-learning techniques have been used. This paper presents an adaptation of the rule-based system NegEx, which uses exact-matching for the aforementioned tasks.
Our contribution consist in assessing the aforementioned two tasks and explored alternatives for each of them, in such a way that the negation detection improves when the entity recognition is able to detect more entities correctly.
The evaluation was carried out within a real domain of 75 EHRs written in Spanish obtaining an f-measure of 76.2 for entity recognition and 73.8 for negation detection.
KeywordsNegation detection Electronic health records Text mining Spanish
The authors would like to thank the personnel of Pharmacy and Pharmacovigilance services of the Galdakao-Usansolo Hospital. This work was partially funded by the Spanish Ministry of Science and Innovation (EXTRECM: TIN2013-46616-C2-1-R, TADEEP: TIN2015-70214-P) and the Basque Government (DETEAMI: Ministry of Health 2014111003, Predoctoral Grant: PRE 2015 1 0211).
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