Étude comparative de serveurs multiterminologiques dans le contexte d’une plateforme d’échange de données de santé ontologiquement annotées

Encore aujourd’hui, l’utilisation de systèmes d’information de santé est souvent problématique en raison de l’absence d’un référentiel sémantique et d’une classification unifiée des données des patients. Cette absence est en grande partie attribuable à l’hétérogénéité des sources sur lesquelles reposent ces systèmes. Cette hétérogénéité nuit en outre au partage des données entre les cliniciens, les chercheurs et les administrateurs au sein et entre les réseaux de santé, partage qui serait pourtant bénéfique autant pour la recherche en santé qu’à la pratique de soins. Une première étape dans la résolution de ces problèmes consiste à classer correctement les données en recourant à des terminologies au moyen de serveurs multiterminologiques (SMT). Ces derniers modélisent les terminologies, en plus de fournir une gamme de services permettant de définir et de mettre en correspondance plusieurs terminologies. Dans ce mémoire, nous cherchons un tel serveur qui aurait le potentiel d’être intégré à une démarche de Système de santé apprenant (SSA) et plus particulièrement à la Plateforme apprenante pour la recherche en santé et services sociaux (PARS3) qui propose un service sûr d’accès aux données de santé ontologiquement annotées. Une méthodologie de comparaison des serveurs a été mise en place afin d’évaluer les performances de chacun en termes d’utilisabilité, ainsi que de capacité à définir de nouvelles terminologies propres à des acteurs particuliers. Ce mémoire utilise une comparaison euristique en regard d’un ensemble de critères basés sur les besoins des utilisateurs, les propriétés des serveurs et des artéfacts qui leur sont liés. Cette comparaison prend en compte les fonctions accessibles à l’utilisateur par l’interface personne-machine ainsi que les fonctions accessibles à des services par l’entremise d’une interface machine-machine. Il est ressorti de cette comparaison qu’aucun des serveurs étudiés ne répondait adéquatement aux besoins. Cela a néanmoins permis d’identifier les fonctionnalités faisant défaut. Finalement, à partir de ces résultats, il a fallu trancher à savoir s’il était plus profitable de développer un serveur propre ou d’améliorer un serveur existant afin qu’il réponde aux exigences requises. Il en résulte que le développement d’un nouveau serveur serait plus avantageux

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Integrating the Human Phenotype Ontology into HeTOP Terminology-Ontology Server.

International audienceThe Human Phenotype Ontology (HPO) is a controlled vocabulary which provides phenotype data related to genes or diseases. The Health Terminology/Ontology Portal (HeTOP) is a tool dedicated to both human beings and computers to access and browse biomedical terminologies or ontologies (T/O). The objective of this work was to integrate the HPO into HeTOP in order to enhance both works. This integration is a success and allows users to search and browse the HPO with a dedicated interface. Furthermore, the HPO has been enhanced with the addition of content such as new synonyms, translations, mappings. Integrating T/O such as the HPO into HeTOP is a benefit to vocabularies because it allows enrichment of them and it is also a benefit for HeTOP which provides a better service to both humans and machines

Evaluation of proteomic and transcriptomic biomarker discovery technologies in ovarian cancer

Novel, specific and sensitive biomarkers are prerequisite to improve diagnosis and prognosis of patients with ovarian cancer. Firstly, a proteomic bottom-up MALDI-TOF mass spectrometric profiling analysis was conducted on a cohort of sixty serum samples specifically collected for this purpose. An in-house stepwise Artificial Neural Network (ANN) algorithm generated a biomarker panel of m/z peaks which differentiated cancer from aged matched controls with an accuracy of 91% and error of 9%, identities were inferred where possible and validation conducted using ELISA on the same cohort. Lack of complete verification, or the ability to verify the full panel lead to an in-depth evaluation of the strategy used with the aim to repeat with an improved methodology. Following this, a feasibility analysis and evaluation was performed on the next generation of equipment for sample fractionation prior to analysis on multiple replicates of stock human serum collected in the same way as the ovarian cohort. The results of which combined with the limited amount of available ovarian cancer sample cohort altered the trajectory of the project to the mining of transcriptomic data acquired from an online data repository. A meta-analysis approach was applied to two carefully selected gene expression microarray data sets ANNs, Cox Univariate Survival analyses and T-tests were used to filter genes whose expression were consistently significantly associated with patient survival times. A list of 56 genes were refined from a potential 37000 gene probes to be taken forward for verification for which more freely available online resources such as SRING, Kaplan Meier Plotter and KEGG were utilised. The list of 56 genes of interest were refined to seven using a larger cohort of transcriptomic data, of the seven one, EDNRA, was selected for translational verification using immunohistochemistry of a tissue microarray of ovarian cancer specimens. Significant association is seen with cancer stage, grade and histology. The merits and flaws of the verification are discussed and future work and direction for research is suggested