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Cyril Grouin

Bio: Cyril Grouin is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Annotation & Conditional random field. The author has an hindex of 21, co-authored 81 publications receiving 1296 citations. Previous affiliations of Cyril Grouin include Centre national de la recherche scientifique.


Papers
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Book ChapterDOI
08 Sep 2015
TL;DR: The third CLEFeHealth evaluation lab as discussed by the authors focused on easing patients and nurses in authoring, understanding, and accessing eHealth information by evaluating methods for information extraction and information retrieval IR.
Abstract: This paper reports on the 3rd CLEFeHealth evaluation lab, which continues our evaluation resource building activities for the medical domain. In this edition of the lab, we focus on easing patients and nurses in authoring, understanding, and accessing eHealth information. The 2015 CLEFeHealth evaluation lab was structured into two tasks, focusing on evaluating methods for information extraction IE and information retrieval IR. The IE task introduced two new challenges. Task 1a focused on clinical speech recognition of nursing handover notes; Task 1b focused on clinical named entity recognition in languages other than English, specifically French. Task 2 focused on the retrieval of health information to answer queries issued by general consumers seeking information to understand their health symptoms or conditions. The number of teams registering their interest was 47 in Tasksi?ź1 2 teams in Task 1a and 7 teams in Task 1b and 53 in Taski?ź2 12 teams for a total of 20 unique teams. The best system recognized 4,i?ź984 out of 6,i?ź818 test words correctly and generated 2,i?ź626 incorrect words i.e., $$38.5 \%$$ error in Task 1a; had the F-measure of 0.756 for plain entity recognition, 0.711 for normalized entity recognition, and 0.872 for entity normalization in Task 1b; and resulted in [email protected] of 0.5394 and [email protected] of 0.5086 in Task 2. These results demonstrate the substantial community interest and capabilities of these systems in addressing challenges faced by patients and nurses. As in previous years, the organizers have made data and tools available for future research and development.

86 citations

Journal ArticleDOI
TL;DR: Text mining approaches are becoming essential to facilitate the automated extraction of useful biomedical information from unstructured text, and it is demonstrated that TM can contribute to complex research tasks in psychiatry.
Abstract: The expansion of biomedical literature is creating the need for efficient tools to keep pace with increasing volumes of information. Text mining (TM) approaches are becoming essential to facilitate the automated extraction of useful biomedical information from unstructured text. We reviewed the applications of TM in psychiatry, and explored its advantages and limitations. A systematic review of the literature was carried out using the CINAHL, Medline, EMBASE, PsycINFO and Cochrane databases. In this review, 1103 papers were screened, and 38 were included as applications of TM in psychiatric research. Using TM and content analysis, we identified four major areas of application: (1) Psychopathology (i.e. observational studies focusing on mental illnesses) (2) the Patient perspective (i.e. patients' thoughts and opinions), (3) Medical records (i.e. safety issues, quality of care and description of treatments), and (4) Medical literature (i.e. identification of new scientific information in the literature). The information sources were qualitative studies, Internet postings, medical records and biomedical literature. Our work demonstrates that TM can contribute to complex research tasks in psychiatry. We discuss the benefits, limits, and further applications of this tool in the future. Copyright © 2015 John Wiley & Sons, Ltd.

75 citations

23 Jun 2011
TL;DR: The definition and novelty of extended named entity annotation guidelines are presented, the human annotation of a global corpus and of a mini reference corpus, and the evaluation of annotations through the computation of inter-annotator agreements are discussed.
Abstract: Within the framework of the construction of a fact database, we defined guidelines to extract named entities, using a taxonomy based on an extension of the usual named entities defini- tion. We thus defined new types of entities with broader coverage including substantive- based expressions. These extended named en- tities are hierarchical (with types and compo- nents) and compositional (with recursive type inclusion and metonymy annotation). Human annotators used these guidelines to annotate a 1.3M word broadcast news corpus in French. This article presents the definition and novelty of extended named entity annotation guide- lines, the human annotation of a global corpus and of a mini reference corpus, and the evalu- ation of annotations through the computation of inter-annotator agreement. Finally, we dis- cuss our approach and the computed results, and outline further work.

69 citations

Proceedings Article
23 Jun 2011
TL;DR: In this article, the authors defined guidelines to extract named entities, using a taxonomy based on an extension of the usual named entities definition, and defined new types of entities with broader coverage including substantive-based expressions.
Abstract: Within the framework of the construction of a fact database, we defined guidelines to extract named entities, using a taxonomy based on an extension of the usual named entities definition. We thus defined new types of entities with broader coverage including substantive-based expressions. These extended named entities are hierarchical (with types and components) and compositional (with recursive type inclusion and metonymy annotation). Human annotators used these guidelines to annotate a 1.3M word broadcast news corpus in French. This article presents the definition and novelty of extended named entity annotation guidelines, the human annotation of a global corpus and of a mini reference corpus, and the evaluation of annotations through the computation of inter-annotator agreements. Finally, we discuss our approach and the computed results, and outline further work.

63 citations

Journal ArticleDOI
TL;DR: This study demonstrates that a simple rule-based system can achieve good performance on the medication extraction task, and shows that controlled modifications (lexicon filtering and rule refinement) were the improvements that best raised the performance.

61 citations


Cited by
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01 Jan 2009

7,241 citations

Journal ArticleDOI
TL;DR: The content of these European Society of Cardiology (ESC) Guidelines has been published for personal and educational use only and no commercial use is authorized.
Abstract: Supplementary Table 9, column 'Edoxaban', row 'eGFR category', '95 mL/min' (page 15). The cell should be coloured green instead of yellow. It should also read "60 mg"instead of "60 mg (use with caution in 'supranormal' renal function)."In the above-indicated cell, a footnote has also been added to state: "Edoxaban should be used in patients with high creatinine clearance only after a careful evaluation of the individual thromboembolic and bleeding risk."Supplementary Table 9, column 'Edoxaban', row 'Dose reduction in selected patients' (page 16). The cell should read "Edoxaban 60 mg reduced to 30 mg once daily if any of the following: creatinine clearance 15-50 mL/min, body weight <60 kg, concomitant use of dronedarone, erythromycin, ciclosporine or ketokonazole"instead of "Edoxaban 60 mg reduced to 30 mg once daily, and edoxaban 30 mg reduced to 15mg once daily, if any of the following: creatinine clearance of 30-50 mL/min, body weight <60 kg, concomitant us of verapamil or quinidine or dronedarone."

4,285 citations

Journal ArticleDOI
TL;DR: The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks, which showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations.

1,111 citations

Journal Article
TL;DR: The Health Insurance Portability and Accountability Act, also known as HIPAA, was designed to protect health insurance coverage for workers and their families while between jobs and establishes standards for electronic health care transactions.
Abstract: The Health Insurance Portability and Accountability Act, also known as HIPAA, was first delivered to congress in 1996 and consisted of just two Titles. It was designed to protect health insurance coverage for workers and their families while between jobs. It establishes standards for electronic health care transactions and addresses the issues of privacy and security when dealing with Protected Health Information (PHI). HIPAA is applicable only in the United States of America.

561 citations