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Medical Entity Recognition and Negation Extraction: Assessment of NegEx on Health Records in Spanish

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Bioinformatics and Biomedical Engineering (IWBBIO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10208))

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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.

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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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Correspondence to Sara Santiso .

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Santiso, S., Casillas, A., Pérez, A., Oronoz, M. (2017). Medical Entity Recognition and Negation Extraction: Assessment of NegEx on Health Records in Spanish. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham.

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  • Print ISBN: 978-3-319-56147-9

  • Online ISBN: 978-3-319-56148-6

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