Paper Info
Title
Detecting negation of medical problems in French clinical notes
Abstract
When automatically mining narrative clinical notes to extract meaningful information, such as medical problems, it is essential to take into account the context of this information. For instance, determining whether medical conditions are negated or not is key information for accurately processing medical reports. This article presents an experiment in adapting the state-of-art NegEx algorithm (Chapman et al.) to the French language and evaluating both algorithms (the original English algorithm and the derived French version) on two clinical corpora (English and French, respectively) annotated for medical problems and their negation status. NegEx is a rule-based algorithm which detects negations of medical problems in English-language medical texts, by looking for specific negation trigger phrases in the context of the medical concepts. Our approach has consisted in designing a new list of trigger phrases in French, by studying examples extracted from French clinical notes and relying on the original English list. We performed an evaluation of the negation detection in both corpora. This study show that the two systems achieve comparable results and good performance (respectively 0.839 and 0.867 F-measure for NegEx and its French adaptation).
Year
DOI
Venue
2012
10.1145/2110363.2110443
IHI
Keywords
Field
DocType
medical concept,french version,french adaptation,french clinical note,english-language medical text,medical condition,medical report,clinical corpus,detecting negation,medical problem,french language,negation,natural language processing,rule based,text mining,english language
Negation,Computer science,French,Narrative,Artificial intelligence,Natural language processing
Conference
Citations 
PageRank 
References 
5
0.46
6
Authors
2
Name
Order
Citations
PageRank
Louise Deleger123420.13
Cyril Grouin217030.22