CNRS Informatics Systems Laboratory

Rouen, France

CNRS Informatics Systems Laboratory

Rouen, France
Time filter
Source Type

Soualmia L.F.,CNRS Informatics Systems Laboratory
Yearbook of medical informatics | Year: 2016

OBJECTIVES: To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain.METHOD: We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review.RESULTS: The selection and evaluation process of this Yearbook's section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowdbased method for ontology engineering.CONCLUSIONS: The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

Flamary R.,CNRS Informatics Systems Laboratory | Rakotomamonjy A.,CNRS Informatics Systems Laboratory
Frontiers in Neuroscience | Year: 2012

One of the most interesting challenges in ECoG-based Brain-Machine Interface is movement prediction. Being able to perform such a prediction paves the way to high-degree precision command for a machine such as a robotic arm or robotic hands. As a witness of the BCI community increasing interest toward such a problem, the fourth BCI Competition provides a dataset which aim is to predict individual finger movements from ECoG signals. The difficulty of the problem relies on the fact that there is no simple relation between ECoG signals and finger movements. We propose in this paper, to estimate and decode these finger flexions using switching models controlled by an hidden state. Switching models can integrate prior knowledge about the decoding problem and helps in predicting fine and precise movements. Our model is thus based on a first block which estimates which finger is moving and another block which, knowing which finger is moving, predicts the movements of all other fingers. Numerical results that have been submitted to the Competition show that the model yields high decoding performances when the hidden state is well estimated. This approach achieved the second place in the BCI competition with a correlation measure between real and predicted movements of 0.42. © 2012 Flamary and Rako-tomamonjy.

Petitjean C.,CNRS Informatics Systems Laboratory | Dacher J.-N.,University of Rouen
Medical Image Analysis | Year: 2011

For the last 15 years, Magnetic Resonance Imaging (MRI) has become a reference examination for cardiac morphology, function and perfusion in humans. Yet, due to the characteristics of cardiac MR images and to the great variability of the images among patients, the problem of heart cavities segmentation in MRI is still open. This paper is a review of fully and semi-automated methods performing segmentation in short axis images using a cardiac cine MRI sequence. Medical background and specific segmentation difficulties associated to these images are presented. For this particularly complex segmentation task, prior knowledge is required. We thus propose an original categorization for cardiac segmentation methods, with a special emphasis on what level of external information is required (weak or strong) and how it is used to constrain segmentation. After reviewing method principles and analyzing segmentation results, we conclude with a discussion and future trends in this field regarding methodological and medical issues. © 2010 Elsevier B.V.

Faro S.,University of Catania | Lecroq T.,CNRS Informatics Systems Laboratory
ACM Computing Surveys | Year: 2013

This article addresses the online exact string matching problem which consists in finding all occurrences of a given pattern p in a text t. It is an extensively studied problem in computer science, mainly due to its direct applications to such diverse areas as text, image and signal processing, speech analysis and recognition, information retrieval, data compression, computational biology and chemistry. In the last decade more than 50 new algorithms have been proposed for the problem, which add up to a wide set of (almost 40) algorithms presented before 2000. In this article we review the string matching algorithms presented in the last decade and present experimental results in order to bring order among the dozens of articles published in this area. © 2013 ACM.

Thiberville L.,CNRS Informatics Systems Laboratory
Respiration; international review of thoracic diseases | Year: 2010

In the past 15 years, new endoscopic methods have been developed in order to improve the detection of early bronchial cancers, with autofluorescence bronchoscopy being the leading technique. However, autofluorescence bronchoscopy is hampered by the low specificity of the fluorescence defect which ranges from 25 to 50%, and its limitation to the proximal bronchial tree from which arise only half of the lung cancers that are currently diagnosed. To overcome these limitations, other techniques emerge including video/autofluorescence bronchoscopy, narrow band imaging, optical coherence tomography, and 'endomicroscopy' using confocal fluorescent laser microscopy. These emerging techniques provide new insight into bronchology, extending the field of exploration from the proximal bronchus down to the most distal part of the lungs, and from macroscopy to in vivo cellular imaging. In the near future, they may enable in vivo, minimally invasive, 'pathological grade' evaluation of abnormal bronchial or parenchymal lung tissue. Whereas promising pioneer work has recently been published, careful assessment is required before these methods find a place in the evaluation strategy of early lung cancer and other lung diseases. Copyright 2010 S. Karger AG, Basel.

Griffon N.,CNRS Informatics Systems Laboratory
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium | Year: 2012

The need for structured data in electronic health records has not been fully addressed by reference terminologies (RT) due to difficulties of use for end-users. Interface terminologies (IT), built for specific usage and users, and linked to RT, may solve this issue. We propose an IT for medical imaging prescription, based on the French nomenclature for procedure (CCAM), and its qualitative evaluation. The creation and evaluation processes were adapted from published guidelines. Prescription IT is available on the web ( It contains 290 orderable terms linked to 249 CCAM codes. The synonymy of prescription IT is significantly richer than the CCAM one and labels are significantly shorter. The main problem came from the CCAM, which is dedicated to billing purposes. We are planning to map prescription IT to other international RT such as RadLex or SNOMED. Prescription IT might quicken the adoption of computerized ordering processes in France.

Grosjean J.,CNRS Informatics Systems Laboratory
Studies in health technology and informatics | Year: 2011

Since the mid-90s, several quality-controlled health gateways were developed. In France, CISMeF is the leading health gateway. It indexes Internet resources from the main institutions, using the MeSH thesaurus and the Dublin Core metadata element set. Since 2005, the CISMeF Information System (IS) includes 24 health terminologies, classifications and thesauri for indexing and information retrieval. This work aims at creating a Health Multi-Terminology Portal (HMTP) and connect it to the CISMeF Terminology Database mainly for searching concepts and terms among all the health controlled vocabularies available in French (or in English and translated in French) and browsing it dynamically. To integrate the terminologies in the CISMeF IS, three steps are necessary: (1) designing a meta-model into which each terminology can be integrated, (2) developing a process to include terminologies into the HMTP, (3) building and integrating existing and new inter-terminology mappings into the HMTP. A total of 24 terminologies are included in the HMTP, with 575,300 concepts, 852,000 synonyms, 222,800 definitions and 1,180,000 relations. Heightteen of these terminologies are not included yet in the UMLS among them, some from the World Health Organization. Since January 2010, HMTP is daily used by CISMeF librarians to index in multi-terminology mode. A health multiterminology portal is a valuable tool helping the indexing and the retrieval of resources from a quality-controlled patient safety gateway. It can also be very useful for teaching or performing audits in terminology management.

Merabti T.,CNRS Informatics Systems Laboratory
Studies in health technology and informatics | Year: 2011

ATC classification is a WHO international classification used to classify drugs. The aim of this paper is to evaluate two lexical methods in English and in French to map ATC to UMLS. Several applications have been impemented to illustrate the use of the ATC mapping in English and French: (a) MeSH translation in Norwegian, (b) Drug Information Portal, and (c) ATC to PubMed tool. Two lexical methods were used to map ATC to UMLS. The first approach used a French natural language processing tool to map French terms of ATC to the French terminologies of UMLS. The second approach used the MetaMap tool to map English terms of ATC to UMLS. The English MetaMap provides slightly more mappings than the French NLP tool (3,170 vs. 2,992). On the other hand, the French NLP tool provides a slightly better precision than MetaMap (88% vs. 86%). Using a manual mapping between ATC and MeSH, the union of the validated mappings between ATC and MeSH provides 2,824 mappings (68.7% of ATC codes of the fifth level). Lexical methods are powerful methods to map health terminologies to the UMLS Metathesaurus. Manual mapping is still necessary to complete the mapping.

Rakotomamonjy A.,CNRS Informatics Systems Laboratory
IEEE Transactions on Signal Processing | Year: 2013

A novel way of solving the dictionary learning problem is proposed in this paper. It is based on a so-called direct optimization as it avoids the usual technique which consists in alternatively optimizing the coefficients of a sparse decomposition and in optimizing dictionary atoms. The algorithm we advocate simply performs a joint proximal gradient descent step over the dictionary atoms and the coefficient matrix. After having derived the algorithm, we also provided in-depth discussions on how the stepsizes of the proximal gradient descent have been chosen. In addition, we uncover the connection between our direct approach and the alternating optimization method for dictionary learning. We have shown that it can be applied to a broader class of non-convex optimization problems than the dictionary learning one. As such, we have denoted the algorithm as a one-step block-coordinate proximal gradient descent. The main advantage of our novel algorithm is that, as suggested by our simulation study, it is more efficient than alternating optimization algorithms. © 2013 IEEE.

Yger F.,CNRS Informatics Systems Laboratory | Rakotomamonjy A.,CNRS Informatics Systems Laboratory
Pattern Recognition | Year: 2011

This paper addresses the problem of optimal feature extraction from a wavelet representation. Our work aims at building features by selecting wavelet coefficients resulting from signal or image decomposition on an adapted wavelet basis. For this purpose, we jointly learn in a kernelized large-margin context the wavelet shape as well as the appropriate scale and translation of the wavelets, hence the name wavelet kernel learning. This problem is posed as a multiple kernel learning problem, where the number of kernels can be very large. For solving such a problem, we introduce a novel multiple kernel learning algorithm based on active constraints methods. We furthermore propose some variants of this algorithm that can produce approximate solutions more efficiently. Empirical analysis show that our active constraint MKL algorithm achieves state-of-the art efficiency. When used for wavelet kernel learning, our experimental results show that the approaches we propose are competitive with respect to the state-of-the-art on braincomputer interface and Brodatz texture datasets. © 2011 Elsevier Ltd. All rights reserved.

Loading CNRS Informatics Systems Laboratory collaborators
Loading CNRS Informatics Systems Laboratory collaborators