Using local grammar for entity extraction from clinical reports

Journal article

Ghoulam, A, Barigou, F, Belalem, G and Meziane, F. 2015. Using local grammar for entity extraction from clinical reports. International Journal of Artificial Intelligence and Interactive Multimedia. Vol 3 (Issue 3), pp. 16 - 24. https://doi.org/10.9781/ijimai.2015.332
AuthorsGhoulam, A, Barigou, F, Belalem, G and Meziane, F.
Abstract

Information Extraction (IE) is a natural language processing (NLP) task whose aim is to analyze texts written in natural language to extract structured and useful information such as named entities and semantic relations linking these entities. Information extraction is an important task for many applications such as bio-medical literature mining, customer care, community websites, and personal information management. The increasing information available in patient clinical reports is difficult to access. As it is often in an unstructured text form, doctors need tools to enable them access to this information and the ability to search it. Hence, a system for extracting this information in a structured form can benefits healthcare professionals. The work presented in this paper uses a local grammar approach to extract medical named entities from French patient clinical reports. Experimental results show that the proposed approach achieved an F-Measure of 90. 06%.

KeywordsInformation Technology ; NLP ; Medical Entities
Year2015
JournalInternational Journal of Artificial Intelligence and Interactive Multimedia
Journal citationVol 3 (Issue 3), pp. 16 - 24
Publisherla universidad en internet
ISSN1989-1660
Digital Object Identifier (DOI)https://doi.org/10.9781/ijimai.2015.332
Web address (URL)http://dx.doi.org/10.9781/ijimai.2015.332
Output statusPublished
Publication datesJun 2015
Publication process dates
Deposited07 Jun 2023
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