Towards Cross Language Morphologic Negation Identification in Electronic Health Records

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 141)

Abstract

The current paper presents an approach for analyzing the Electronic Health Records (EHRs) with the goal of automatically identifying morphologic negation such that swapping the truth values of concepts introduced by negation does not interfere with understanding the medical discourse. To identify morphologic negation we propose the RoPreNex strategy that represents the adaptation of our PreNex approach to the Romanian language [1]. We evaluate our proposed solution on the MTsamples [2] dataset. The results we obtained are promising and ensure a reliable negation identification approach in medical documents. We report precision of 92.62 % and recall of 93.60 % in case of the morphologic negation identification for the source language and an overall performance in the morphologic negation identification of 77.78 % precision and 80.77 % recall in case of the target language.

Keywords

Cross language Morphologic negation Electronic health records Dictionary 

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Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  1. 1.Computer Science DepartamentTechnical University of Cluj-NapocaCluj-NapocaRomania

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