Using Local Grammar for Entity Extraction from Clinical Reports - Ghoulam, Aicha; Barigou, Fatiha; Belalem, Ghalem; Meziane, Farid (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 2020)
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%.
Keywords: information extraction; electronic clinical reports; medical entities recognition; natural language processing; IJIMAI