[Show abstract][Hide abstract] ABSTRACT: Unlabelled:
Identifying patients with Fibromuscular Dysplasia (FMD) at the international level will have considerable value for understanding the epidemiology, clinical manifestations and susceptible genes in this arterial disease, but also for identifying eligible patients in clinical trials or cohorts. We present a two-step methodology to create a general semantic interoperability framework allowing access and comparison of distributed data over various nations, languages, formats and databases.
The first step is to develop a pivot multidimensional model based on a core dataset to harmonize existing heterogeneous data sources. The second step is to align the model to additional data, semantically related to FMD and collected currently in various registries. We present the results of the first step that has been fully completed with the validation and implementation of the model in a dedicated information system (SIR-FMD). We discuss the current achievements for step 2 and the extensibility of the methodology in the context of other rare diseases.
Full-text · Article · Aug 2015 · Studies in health technology and informatics
[Show abstract][Hide abstract] ABSTRACT: The underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients' experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance.
A scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance.
Daubt et al's recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2.
Of the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified.
This scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system.
Full-text · Article · Jul 2015 · Journal of Medical Internet Research
[Show abstract][Hide abstract] ABSTRACT: The SIR-FMD project is a partnership between the Department of Genetics and Reference Centre for Rare Vascular Diseases at the Georges Pompidou European Hospital in Paris and the Medical Informatics and Knowledge Engineering Laboratory of Inserm. Its aim is to use an ontological approach to implement an information system for the French Fibromuscular Dysplasia Registry. The existing data was dispersed in numerous databases, which had been created independently. These databases have different structures and contain data of diverse quality. The project aims to provide generic solutions for the management of the communication of medical data. The secondary objective is to demonstrate the applicability of these generic solutions in the field of rare diseases (RD) in an operational context. The construction of the French FMD registry was a multistep process. A secure platform has been available since the beginning of November 2013. The medical records of 471 patients from the initial dataset provided by the HEGP-Paris, France have been included, and are accessible from a secure user account. Users are organized into a collaborative group, and can access patient groups. Each electronic patient record contains more than 2,200 items. The problem of semantic interoperability has become one of the major challenges for the development of applications requiring the sharing and reuse of data. The information system component of the SIR-FMD project has a direct impact on the standardisation of coding of rare diseases and thereby contributes to the development of e-Health.
Full-text · Article · May 2015 · Studies in health technology and informatics
[Show abstract][Hide abstract] ABSTRACT: Personalized medicine is a broad and rapidly advancing field of health care that is informed by each person's unique clinical, genetic, genomic, and environmental information. Health care that embraces personalized medicine is an integrated, coordinated, evidence based approach to individualizing patient care across the continuum. It is very important to make the right treatment decision but before that to obtain a good diagnosis. There are several clinical forms of disease whose symptoms vary depending on the age and etiology. In this study, we investigated and evaluated a model framework, for personalized diagnostic decisions, based on Case Based Fuzzy Cognitive Map (CBFCM, a cognitive process applying the main features of fuzzy logic and neural processors to situations involving imprecision and uncertain descriptions, in a similar way to intuitive human reasoning. We explored the use of this method for modelling clinical practice guidelines.
No preview · Article · May 2015 · Studies in health technology and informatics
[Show abstract][Hide abstract] ABSTRACT: Objectives: An important barrier to electronic healthcare information exchanges (HIE) is the lack of interoperability between information systems especially on the semantic level. In the scope of the ANR (Agence Nationale pour la Recherche) / TERSAN (Terminology and Data Elements Repositories for Healthcare Interoperability) project, we propose to set and use a semantic interoperability platform, based on semantic web technologies, in order to facilitate standardized healthcare information exchanges between heterogeneous Electronic Healthcare Records (EHRs) in different care settings.
Material and methods: The platform is a standard-based expressive and scalable semantic interoperability framework. It includes centrally managed Common Data Elements bounded to international/national reference terminologies such as ICD10, CCAM, SNOMED CT, ICD-O, LOINC and PathLex. It offers semantic services such as dynamic mappings between reference and local terminologies.
Results: A pilot implementation of semantic services was developed and evaluated within a HIE prototype in telepathology for remote expert advice. The semantic services developed for transcoding local terms into reference terms take into account the type of message and the exchange context defined within standard-based integration profiles.
Conclusion: The TERSAN platform is an innovative semantic interoperability framework that (1) provides standard-based semantic services applicable to any HIE infrastructure and (2) preserves the use of local terminologies and local models by end users (health professional’s priority).
[Show abstract][Hide abstract] ABSTRACT: Decision making in the field of medical diagnosis involves a degree of uncertainty and a need to take into account
the patient’s clinical parameters, the context of illness and the medical knowledge of the physician, to determine and
confirm the diagnosis. In this study, we investigated and evaluated a model framework, for diagnostic decisions,
based on a cognitive process and a Semantic Web approach. Fuzzy cognitive maps (FCM) are a cognitive process
applying the main features of fuzzy logic and neural processors to situations involving imprecision and uncertain
descriptions, in a similar way to intuitive human reasoning. We explored the use of this method for modeling clinical
practice guidelines, using Semantic Web tools to implement these guidelines and for the formalization process.
Twenty-five clinical and 13 diagnosis concepts were identified, to represent the problem of urinary tract infection
Full-text · Article · Jan 2015 · Journal of Computer Science & Systems Biology
[Show abstract][Hide abstract] ABSTRACT: Antibiotics resistance poses a significant problem in today’s hospital care. Although large amounts of resistance data are gathered locally, they cannot be compared globally due to format and access diversity.
We present an ontology-based integration approach serving an EU project in making antibiotics resistance data semantically and geographically interoperable. We particularly focus on EU-wide clinical data integration for real-time antibiotic resistance surveillance. The data semantics is formalized by multiple layers of terminology-bound description logic ontologies. Local database-to-RDF (D2R) converters, normalizers and data wrapper ontologies render hospital data accessible to SPARQL queries, which populate a mediator layer. This semiformal data is then integrated and rendered comparable via formal OWL domain ontologies and rule-driven reasoning applications. The presented integration layer enables clinical data miners to query over multiple hospitals which behave like one homogeneous ‘virtual clinical information system’. We show how cross-site querying can be achieved across borders, languages and different data models. Aside the drawbacks, we elaborate on the unique advantages over comparable previous efforts, i.e. tackling real-time data access and scalability.
[Show abstract][Hide abstract] ABSTRACT: Evaluation and validation have become a crucial problem for the development of semantic resources. We developed Ci4SeR, a Graphical User Interface to optimize the curation work (not taking into account structural aspects), suitable for any type of resource with lightweight description logic. We tested it on OntoADR, an ontology of adverse drug reactions. A single curator has reviewed 326 terms (1020 axioms) in an estimated time of 120 hours (2.71 concepts and 8.5 axioms reviewed per hour) and added 1874 new axioms (15.6 axioms per hour). Compared with previous manual endeavours, the interface allows increasing the speed-rate of reviewed concepts by 68% and axiom addition by 486%. A wider use of Ci4SeR would help semantic resources curation and improve completeness of knowledge modelling.
No preview · Article · Aug 2014 · Studies in health technology and informatics
[Show abstract][Hide abstract] ABSTRACT: Numerous hospitals contain unexploited knowledge deposits. These often take the form of unstructured records with heterogeneous content, which, at various levels of those organizations, register past cases. Those records are for instance patient medical records. Accessing the knowledge and experience they gather would help us to handle present cases. We present here a method to normalize textual reports in foetopathology in order to constitute a proper case base that will be the target of case-based reasoning techniques. Statistics of noise and silence generated by this method on 10 cases are presented.
Full-text · Article · Aug 2014 · Studies in health technology and informatics
[Show abstract][Hide abstract] ABSTRACT: Robust alignments between ICD and MedDRA are essential to enable the secondary use of clinical data for pharmacovigilance research. UMLS makes available ICD-to-MedDRA mappings, but they are only poorly specified, which introduces difficulties when exploited in an automatic way. SKOS vocabulary can help achieve quality and machine-processable mappings. We have developed an algorithm based on several simple rules which annotates automatically ICD-to-MedDRA mappings with SKOS predicates. The method was tested and evaluated on a sample of ICD-10-to MedDRA mappings extracted from UMLS. The algorithm demonstrated satisfying performances, especially for skos:exactMatch properties, which suggests that automatic methods can be used to improve the quality of terminology mappings.
Full-text · Article · Aug 2014 · Studies in health technology and informatics
[Show abstract][Hide abstract] ABSTRACT: In the era of data sharing and systems interoperability, the automation of data schema alignment has become a priority. Discovering data mappings is the aim of many alignment approaches that have been described in the literature and the effectiveness of which depends on data specifications. In this context, we propose a method for mappings formalization that allows automated data integration processes optimization. This method, involving both data element level and value element level, allows an automated inference of mappings expressed by rules. In this paper, we start by describing the methods used to achieve this mappings formalization. Then, we explain how it has been validated by characterizing data from two use cases. We end up by discussing the objectives of the proposed formalization.
[Show abstract][Hide abstract] ABSTRACT: De nombreux hôpitaux contiennent encore des dépôts de connaissances inexploités. Ceux-ci prennent souvent la forme d'enregistrements textuels faiblement structurés aux contenus hétérogènes, qui contiennent des cas passés. Ces enregistrements sont par exemple les comptes rendus d'examens médicaux. L'accès aux connaissances et à l'expérience qu'ils renferment pourrait aider à traiter les cas présents. Nousprésentons ici une méthode pour à normaliser la représentation de comptes rendus textuels en foetopathologie de manière à constituer une base qui sera utilisée pour raisonner à partir de cas. Cette méthode se base sur la transformation des comptes rendus en arbres. Les mesures statistiques concernant le bruit et le silence générés sur 10 de nos cas sont présentées.
[Show abstract][Hide abstract] ABSTRACT: Although MedDRA has obvious advantages over previous terminologies for coding adverse drug reactions and discovering potential signals using data mining techniques, its terminological organization constrains users to search terms according to predefined categories. Adding formal definitions to MedDRA would allow retrieval of terms according to a case definition that may correspond to novel categories that are not currently available in the terminology. To achieve semantic reasoning with MedDRA, we have associated formal definitions to MedDRA terms in an OWL file named OntoADR that is the result of our first step for providing an "ontologized" version of MedDRA. MedDRA five-levels original hierarchy was converted into a subsumption tree and formal definitions of MedDRA terms were designed using several methods: mappings to SNOMED-CT, semi-automatic definition algorithms or a fully manual way. This article presents the main steps of OntoADR conception process, its structure and content, and discusses problems and limits raised by this attempt to "ontologize" MedDRA.
Full-text · Article · Mar 2014 · Journal of Biomedical Informatics
[Show abstract][Hide abstract] ABSTRACT: Objective: Non-alcoholic fatty liver disease (NAFLD) is a recently recognized entity related to modern lifestyle and with expanded clinical importance because of the rising in-cidence of obesity and diabetes. Methods: We have developed a framework for interacting with patient's heterogeneous data (omics, clinical and bi-ological information) and formalizing medical knowledge. Results: In this paper we present new diagnosis model to predicate NASH. We extracted 18 clinical concepts and these concepts are annotated with SNOMED CT concepts. We tested our diagnostic model with database of 36 pa-tients. We have a performance of 91%. Conclusion: This work represents a preliminary step in de-veloping a CDSS and we'll use a clinical database to test this system and to compare it with others statistic reason-ing methods.
[Show abstract][Hide abstract] ABSTRACT: Cet article traite de la problématique de partage de données biomédicales dans le cadre du suivi de l’évolution de la résistance des bactéries aux antibiotiques en Europe. Dans ce domaine, les questions suivantes se posent : comment partager de l’information biomédicale de manière non ambiguë, en temps réel, et à la demande en Europe ? Ces questions sont associées à diverses problématiques telles que la qualité des données à partager, leur représentation à travers leur structure, leur vocabulaire et leur sémantique. Nous abordons aussi les problèmes d’alignement de données aux ontologies de domaine et de la fédération de données aidée d’ontologies. Enfin, nous présentons un système d’interopérabilité sémantique fondé sur des règles et qui aborde le problème de l’alignement sémantique de systèmes hétérogènes appliqué à notre domaine. Nous discutons finalement de l’apport de la sémantique pour le partage d’information et des limites des outils et méthodes actuels.
Full-text · Article · Dec 2013 · Ingénierie des systèmes d information
[Show abstract][Hide abstract] ABSTRACT: This study aimed to focus on medical knowledge representation and reasoning using the probabilistic and fuzzy influence processes, implemented in the semantic web, for decision support tasks. Bayesian belief networks (BBNs) and fuzzy cognitive maps (FCMs), as dynamic influence graphs, were applied to handle the task of medical knowledge formalization for decision support. In order to perform reasoning on these knowledge models, a general purpose reasoning engine, EYE, with the necessary plug-ins was developed in the semantic web. The two formal approaches constitute the proposed decision support system (DSS) aiming to recognize the appropriate guidelines of a medical problem, and to propose easily understandable course of actions to guide the practitioners. The urinary tract infection (UTI) problem was selected as the proof-of-concept example to examine the proposed formalization techniques implemented in the semantic web. The medical guidelines for UTI treatment were formalized into BBN and FCM knowledge models. To assess the formal models' performance, 55 patient cases were extracted from a database and analyzed. The results showed that the suggested approaches formalized medical knowledge efficiently in the semantic web, and gave a front-end decision on antibiotics' suggestion for UTI.
Full-text · Article · Aug 2013 · Computer methods and programs in biomedicine