Oc031--Generic Substitution Of Antiepileptic Drug (Aed) And Loss Of Seizure Control: A Population-Based Case-Crossover Study

Open access CC-BYInternational audienceThere are still controversies over pill substitution among AEDs: some studies claimed that switching between brand and generic AED (generic substitution) can lead to breakthrough seizures; other studies have refuted these concerns. France and some US states recommend limiting substitution of generic AED. We aimed at further estimating the association between generic substitution and loss of seizure control

Extraction de chroniques discriminantes

International audienceL'extraction de motifs séquentiels vise à extraire des comportements récurrents dans un ensemble de séquences. Lorsque ces séquences sont étiquetées, l'extraction de motifs discriminants engendre des motifs caractéristiques de chaque classe de séquences. Cet article s'intéresse à l'extraction des chroniques discriminantes où une chronique est un type de motif temporel représentant des durées inter-évènements quantitatives. L'article présente l'algorithme DCM dont l'originalité réside dans l'utilisation de méthodes d'apprentissage automatique pour extraire les intervalles temporels. Les performances computationnelles et le pouvoir discriminant des chroniques extraites sont évalués sur des données synthétiques et réelles

An extension of chronicles temporal model with taxonomies: Application to epidemiological studies

International audienceMedico-administrative databases contain information about patients’ medical events, i.e. their care trajectories. Semantic Web technologies are used by epidemiologists to query these databases in order to identify patients whose care trajectories conform to some criteria. In this article we are interested in care trajectories involving temporal constraints. In such cases, Semantic Web tools lack computational efficiency while temporal pattern matching algorithms are efficient but lack of expressiveness. We propose to use a temporal pattern called chronicles to represent temporal constraints on care trajectories. We also propose an hybrid approach, combining the expressiveness of SPARQL and the efficiency of chronicle recognition to query care trajectories. We evaluate our approach on synthetic data and real large data. The results show that the hybrid approach is more efficient than pure SPARQL, and validate the interest of our tool to detect patients having venous thromboembolism disease in the French medico-administrative database

IA et données de santé : regards croisés sur les droits, devoirs et responsabilités des personnes publiques

National audienc

Contribution à l'amélioration du codage en médecine (apports de la reconnaissance vocale et de l'indexation automatique de documents)

RENNES1-BU Santé (352382103) / SudocSudocFranceF

From medico-administrative databases analysis to care trajectories analytics: an example with the French SNDS

International audienceMedico-administrative data like SNDS (Système National de Données de Santé) are not collected initially for epidemiological purposes. Moreover, the data model and the tools proposed to SNDS users make their in-depth exploitation difficult. We propose a data model, called the ePEPS model, based on healthcare trajectories to provide a medical view of raw data. A data abstraction process enables the clini-cian to have an intuitive medical view of raw data and to design a study-specific view. This view is based on a generic model of care trajectory, that is a sequence of time stamped medical events for a given patient. This model is combined with tools to manipulate care trajectories efficiently. I N T R O D U C T I O N Medico-administrative databases hold rich information about healthcare trajectories (or healthcare pathways) at an individual level. Such data are very valuable for carrying out pharmaco-epidemiological studies on large representative cohorts of patients in real-life conditions. Moreover, historical data are readily available for longitudinal analysis of care trajectories. These opportunities are given by the use of the database of the French healthcare system, so called SNDS (Syst eme National de Donn ees de Sant e) database, which covers 98.8% of the French population, with a sliding period of 3 years. A classical pharmaco-epidemiological study from medico-administrative databases consists of three main steps: (i) defining inclusion and exclusion criteria of a cohort, (ii) specifying proxies for events of interest, and (iii) analyzing the transformed data. Practically, these three steps are closely intertwined and make use of digital data management tools (e.g., SQL databases, R, or SAS). The study outcomes depend on the available data at hand as much as on the tools to manage and process them. But the data model, 1 designed for administrative purposes , is not suitable for pharmaco-epidemiological studies without careful data preparation. It leads to difficulties for epidemiologists to access the useful information and even to know what is reachable with such databases. For instance, the SNDS database is a rela-tional database with hundreds of tables with very complex join relations. The set of prescribed drugs of a patient is accessible with a query containing 10 join relations involving attributes with unintuitive names. Mastering the data management with such complex models requires a lot of time, good knowledge of its content, and some technical skills. It is a practical bottleneck to exploit the potential of the database. 1 A data model is an abstract model that describes the organization of the data. In relational database, it is the description of tables, their attributes, and their relations. ª 2017 Soci et

Extraction de chroniques discriminantes

International audienceL'extraction de motifs séquentiels vise à extraire des comportements récurrents dans un ensemble de séquences. Lorsque ces séquences sont étiquetées, l'extraction de motifs discriminants engendre des motifs caractéristiques de chaque classe de séquences. Cet article s'intéresse à l'extraction des chroniques discriminantes où une chronique est un type de motif temporel représentant des durées inter-évènements quantitatives. L'article présente l'algorithme DCM dont l'originalité réside dans l'utilisation de méthodes d'apprentissage automatique pour extraire les intervalles temporels. Les performances computationnelles et le pouvoir discriminant des chroniques extraites sont évalués sur des données synthétiques et réelles

Discriminant chronicles mining: Application to care pathways analytics

International audiencePharmaco-epidemiology (PE) is the study of uses and effects of drugs in well defined populations. As medico-administrative databases cover a large part of the population, they have become very interesting to carry PE studies. Such databases provide longitudinal care pathways in real condition containing timestamped care events, especially drug deliveries. Temporal pattern mining becomes a strategic choice to gain valuable insights about drug uses. In this paper we propose DCM , a new discriminant temporal pattern mining algorithm. It extracts chronicle patterns that occur more in a studied population than in a control population. We present results on the identification of possible associations between hospitalizations for seizure and anti-epileptic drug switches in care pathway of epileptic patients

Extraction de chroniques discriminantes

International audienceL'extraction de motifs séquentiels vise à extraire des comportements récurrents dans un ensemble de séquences. Lorsque ces séquences sont étiquetées, l'extraction de motifs discriminants engendre des motifs caractéristiques de chaque classe de séquences. Cet article s'intéresse à l'extraction des chroniques discriminantes où une chronique est un type de motif temporel représentant des durées inter-évènements quantitatives. L'article présente l'algorithme DCM dont l'originalité réside dans l'utilisation de méthodes d'apprentissage automatique pour extraire les intervalles temporels. Les performances computationnelles et le pouvoir discriminant des chroniques extraites sont évalués sur des données synthétiques et réelles

Discriminant chronicle mining

International audienceSequential pattern mining attempts to extract frequent behaviors from a sequential dataset. When sequences are labeled, it is interesting to extract behaviors that characterize each sequence class. This task is called discriminant pattern mining. In this paper, we introduce discriminant chronicle mining. Conceptually, a chronicle is a temporal graph whose vertices are events and whose edges represent numerical temporal constraints between these events. We propose DCM, an algorithm that mines discriminant chronicles. It is based on rule learning methods that extract the temporal constraints. Computational performances and discriminant power of extracted chronicles are evaluated on synthetic and real data. Finally, we apply this algorithm to the case study consisting in analyzing care pathways of epileptic patients