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362 results on '"Rey, Grégoire"'

2. A Deep Artificial Neural Network−Based Model for Prediction of Underlying Cause of Death From Death Certificates: Algorithm Development and Validation

Author

Falissard, Louis, Morgand, Claire, Roussel, Sylvie, Imbaud, Claire, Ghosn, Walid, Bounebache, Karim, and Rey, Grégoire

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundCoding of underlying causes of death from death certificates is a process that is nowadays undertaken mostly by humans with potential assistance from expert systems, such as the Iris software. It is, consequently, an expensive process that can, in addition, suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the rise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems that were typically considered out of reach without human assistance; they require a considerable amount of data to learn from, which is typically their main limiting factor. However, the CépiDc (Centre d’épidémiologie sur les causes médicales de Décès) stores an exhaustive database of death certificates at the French national scale, amounting to several millions of training examples available for the machine learning practitioner. ObjectiveThis article investigates the application of deep neural network methods to coding underlying causes of death. MethodsThe investigated dataset was based on data contained from every French death certificate from 2000 to 2015, containing information such as the subject’s age and gender, as well as the chain of events leading to his or her death, for a total of around 8 million observations. The task of automatically coding the subject’s underlying cause of death was then formulated as a predictive modelling problem. A deep neural network−based model was then designed and fit to the dataset. Its error rate was then assessed on an exterior test dataset and compared to the current state-of-the-art (ie, the Iris software). Statistical significance of the proposed approach’s superiority was assessed via bootstrap. ResultsThe proposed approach resulted in a test accuracy of 97.8% (95% CI 97.7-97.9), which constitutes a significant improvement over the current state-of-the-art and its accuracy of 74.5% (95% CI 74.0-75.0) assessed on the same test example. Such an improvement opens up a whole field of new applications, from nosologist-level batch-automated coding to international and temporal harmonization of cause of death statistics. A typical example of such an application is demonstrated by recoding French overdose-related deaths from 2000 to 2010. ConclusionsThis article shows that deep artificial neural networks are perfectly suited to the analysis of electronic health records and can learn a complex set of medical rules directly from voluminous datasets, without any explicit prior knowledge. Although not entirely free from mistakes, the derived algorithm constitutes a powerful decision-making tool that is able to handle structured medical data with an unprecedented performance. We strongly believe that the methods developed in this article are highly reusable in a variety of settings related to epidemiology, biostatistics, and the medical sciences in general.

Published
2020
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5. Recent historic increase of infant mortality in France: A time-series analysis, 2001 to 2019

Author

Trinh, Nhung TH, de Visme, Sophie, Cohen, Jérémie F, Bruckner, Tim, Lelong, Nathalie, Adnot, Pauline, Rozé, Jean-Christophe, Blondel, Béatrice, Goffinet, François, Rey, Grégoire, Ancel, Pierre-Yves, Zeitlin, Jennifer, and Chalumeau, Martin

Subjects
Biomedical and Clinical Sciences, Public Health, Health Sciences, Pediatric, Good Health and Well Being, Infant mortality, Joinpoints regression, Interrupted time-series analysis
Abstract

BackgroundThe infant mortality rate (IMR) serves as a key indicator of population health.MethodsWe used data from the French National Institute of Statistics and Economic Studies on births and deaths during the first year of life from 2001 to 2019 to calculate IMR aggregated by month. We ran joinpoint regressions to identify inflection points and assess the linear trend of each segment. Exploratory analyses were performed for overall IMR, as well as by age at death subgroups (early neonatal [D0-D6], late neonatal [D7-27], and post-neonatal [D28-364]), and by sex. We performed sensitivity analyses by excluding deaths at D0 and using other time-series modeling strategies.ResultsOver the 19-year study period, 53,077 infant deaths occurred, for an average IMR of 3·63/1000 (4·00 in male, 3·25 in female); 24·4% of these deaths occurred during the first day of life and 47·8% during the early neonatal period. Joinpoint analysis identified two inflection points in 2005 and 2012. The IMR decreased sharply from 2001 to 2005 (slope: -0·0167 deaths/1000 live births/month; 95%CI: -0·0219 to -0·0116) and then decreased slowly between 2005 and 2012 (slope: -0·0041; 95%CI: -0·0065 to -0·0016). From 2012 onwards, a significant increase in IMR was observed (slope: 0·0033; 95%CI: 0·0011 to 0·0056). Subgroup analyses indicated that these trends were driven notably by an increase in the early neonatal period. Sensitivity analyses provided consistent results.InterpretationThe recent historic increase in IMR since 2012 in France should prompt urgent in-depth investigation to understand the causes and prepare corrective actions.FundingNo financial relationships with any organizations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.

Published
2022

6. Learning a binary search with a recurrent neural network. A novel approach to ordinal regression analysis

Author

Falissard, Louis, Bounebache, Karim, and Rey, Grégoire

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Deep neural networks are a family of computational models that are naturally suited to the analysis of hierarchical data such as, for instance, sequential data with the use of recurrent neural networks. In the other hand, ordinal regression is a well-known predictive modelling problem used in fields as diverse as psychometry to deep neural network based voice modelling. Their specificity lies in the properties of their outcome variable, typically considered as a categorical variable with natural ordering properties, typically allowing comparisons between different states ("a little" is less than "somewhat" which is itself less than "a lot", with transitivity allowed). This article investigates the application of sequence-to-sequence learning methods provided by the deep learning framework in ordinal regression, by formulating the ordinal regression problem as a sequential binary search. A method for visualizing the model's explanatory variables according to the ordinal target variable is proposed, that bears some similarities to linear discriminant analysis. The method is compared to traditional ordinal regression methods on a number of benchmark dataset, and is shown to have comparable or significantly better predictive power.

Published
2021

8. Neural translation and automated recognition of ICD10 medical entities from natural language

Author

Falissard, Louis, Morgand, Claire, Roussel, Sylvie, Imbaud, Claire, Ghosn, Walid, Bounebache, Karim, and Rey, Grégoire

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

The recognition of medical entities from natural language is an ubiquitous problem in the medical field, with applications ranging from medical act coding to the analysis of electronic health data for public health. It is however a complex task usually requiring human expert intervention, thus making it expansive and time consuming. The recent advances in artificial intelligence, specifically the raise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems, with the notable example of neural sequence models and their powerful applications in natural language processing. They however require a considerable amount of data to learn from, which is typically their main limiting factor. However, the C\'epiDc stores an exhaustive database of death certificates at the French national scale, amounting to several millions of natural language examples provided with their associated human coded medical entities available to the machine learning practitioner. This article investigates the applications of deep neural sequence models to the medical entity recognition from natural language problem.

Published
2020

9. A deep artificial neural network based model for underlying cause of death prediction from death certificates

Author

Falissard, Louis, Morgand, Claire, Roussel, Sylvie, Imbaud, Claire, Ghosn, Walid, Bounebache, Karim, and Rey, Grégoire

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Applications
Abstract

Underlying cause of death coding from death certificates is a process that is nowadays undertaken mostly by humans with a potential assistance from expert systems such as the Iris software. It is as a consequence an expensive process that can in addition suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the raise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problem that were typically considered as out of reach without human assistance. They however require a considerable amount of data to learn from, which is typically their main limiting factor. However, the C\'epiDc stores an exhaustive database of death certificate at the French national scale, amounting to several millions training example available for the machine learning practitioner. This article presents a deep learning based tool for automated coding of the underlying cause of death from the data contained in death certificates with 97.8% accuracy, a substantial achievement compared to the Iris software and its 75% accuracy assessed on the same test examples. Such an improvement opens a whole field of new applications, from nosologist-level batch automated coding to international and temporal harmonization of cause of death statistics., Comment: 25 pages, 12 figures

Published
2019

18. An empirical comparative study of approximate methods for binary graphical models; application to the search of associations among causes of death in French death certificates

Author

Viallon, Vivian, Banerjee, Onureena, Rey, Gregoire, Jougla, Eric, and Coste, Joel

Subjects
Statistics - Machine Learning, Statistics - Applications, Statistics - Methodology
Abstract

Looking for associations among multiple variables is a topical issue in statistics due to the increasing amount of data encountered in biology, medicine and many other domains involving statistical applications. Graphical models have recently gained popularity for this purpose in the statistical literature. Following the ideas of the LASSO procedure designed for the linear regression framework, recent developments dealing with graphical model selection have been based on $\ell_1$-penalization. In the binary case, however, exact inference is generally very slow or even intractable because of the form of the so-called log-partition function. Various approximate methods have recently been proposed in the literature and the main objective of this paper is to compare them. Through an extensive simulation study, we show that a simple modification of a method relying on a Gaussian approximation achieves good performance and is very fast. We present a real application in which we search for associations among causes of death recorded on French death certificates., Comment: 29 pages, 4 figures.

Published
2010

32. Mapping and predicting mortality from systemic sclerosis

Author

Elhai, Muriel, Meune, Christophe, Boubaya, Marouane, Avouac, Jérôme, Hachulla, Eric, Balbir-Gurman, Alexandra, Riemekasten, Gabriela, Airò, Paolo, Joven, Beatriz, Vettori, Serena, Cozzi, Franco, Ullman, Susanne, Czirják, László, Tikly, Mohammed, Müller-Ladner, Ulf, Caramaschi, Paola, Distler, Oliver, Iannone, Florenzo, Ananieva, Lidia P, Hesselstrand, Roger, Becvar, Radim, Gabrielli, Armando, Damjanov, Nemanja, Salvador, Maria J, Riccieri, Valeria, Mihai, Carina, Szücs, Gabriella, Walker, Ulrich A, Hunzelmann, Nicolas, Martinovic, Duska, Smith, Vanessa, Müller, Carolina de Souza, Montecucco, Carlo Maurizio, Opris, Daniela, Ingegnoli, Francesca, Vlachoyiannopoulos, Panayiotis G, Stamenkovic, Bojana, Rosato, Edoardo, Heitmann, Stefan, Distler, Jörg H W, Zenone, Thierry, Seidel, Matthias, Vacca, Alessandra, Langhe, Ellen De, Novak, Srdan, Cutolo, Maurizio, Mouthon, Luc, Henes, Jörg, Chizzolini, Carlo, Mühlen, Carlos Alberto von, Solanki, Kamal, Rednic, Simona, Stamp, Lisa, Anic, Branimir, Santamaria, Vera Ortiz, Santis, Maria De, Yavuz, Sule, Sifuentes-Giraldo, Walter Alberto, Chatelus, Emmanuel, Stork, Jiri, Laar, Jacob van, Loyo, Esthela, García de la Peña Lefebvre, Paloma, Eyerich, Kilian, Cosentino, Vanesa, Alegre-Sancho, Juan Jose, Kowal-Bielecka, Otylia, Rey, Grégoire, Matucci-Cerinic, Marco, and Allanore, Yannick

Published
2017
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34. Recent historic increase of infant mortality in France: A time-series analysis, 2001 to 2019

Author

Trinh, Nhung T.H., primary, de Visme, Sophie, additional, Cohen, Jérémie F., additional, Bruckner, Tim, additional, Lelong, Nathalie, additional, Adnot, Pauline, additional, Rozé, Jean-Christophe, additional, Blondel, Béatrice, additional, Goffinet, François, additional, Rey, Grégoire, additional, Ancel, Pierre-Yves, additional, Zeitlin, Jennifer, additional, and Chalumeau, Martin, additional

Published
2022
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