51 research outputs found
Query expansion using medical information extraction for improving information retrieval in French medical domain
Many users’ queries contain references to named entities, and this is particularly true in the medical field. Doctors express their information needs using medical entities as they are elements rich with information that helps to better target the relevant documents. At the same time, many resources have been recognized as a large container of medical entities and relationships between them such as clinical reports; which are medical texts written by doctors. In this paper, we present a query expansion method that uses medical entities and their semantic relations in the query context based on an external resource in OWL. The goal of this method is to evaluate the effectiveness of an information retrieval system to support doctors in accessing easily relevant information. Experiments on a collection of real clinical reports show that our approach reveals interesting improvements in precision, recall and MAP in medical information retrieval
Modelling and simulation of flow and agglomeration in deep veins valves using discrete multi physics
Document Level Sentiment Analysis: A survey
Sentiment analysis becomes a very active research area in the text mining field. It aims to extract people's opinions, sentiments, and subjectivity from the texts. Sentiment analysis can be performed at three levels: at document level, at sentence level and at aspect level. An important part of research effort focuses on document level sentiment classification, including works on opinion classification of reviews. This survey paper tackles a comprehensive overview of the last update of sentiment analysis at document level. The main target of this survey is to give nearly full image of sentiment analysis application, challenges and techniques at this level. In addition, some future research issues are also presented
Using local grammar for entity extraction from clinical reports
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%
Nouveautés dans la stéatose non alcoolique du foie (NAFLD) [New trends in non-alcoholic fatty liver diseases (NAFLD)]
Non-alcoholic fatty liver disease (NAFLD) is one of the most prevalent liver diseases with an epidemiology correlated to obesity and metabolic syndrome. The last decade was rich of significant advances in understanding the pathophysiology of the disease, linking environmental elements, genetic factors and microbiota modifications, as well as in staging, screening and therapeutic development. The purpose of this article is to summarize recent advances in the field of NAFLD, on her way to become the first cause of cirrhosis and liver transplantation worldwide
Using Local Grammar for Entity Extraction from Clinical Reports
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%
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