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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.

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.

International audience; Computational signal detection constitutes a key element of postmarketing drug monitoring and surveillance. Diverse data sources are considered within the 'search space' of pharmacovigilance scientists, and respective data analysis methods are employed, all with their qualities and shortcomings, towards more timely and accurate signal detection. Recent systematic comparative studies highlighted not only event-based and data-source-based differential performance across methods but also their complementarity. These findings reinforce the arguments for exploiting all possible information sources for drug safety and the parallel use of multiple signal detection methods. Combinatorial signal detection has been pursued in few studies up to now, employing a rather limited number of methods and data sources but illustrating well-promising outcomes. However, the large-scale realization of this approach requires systematic frameworks to address the challenges of the concurrent analysis setting. In this paper, we argue that semantic technologies provide the means to address some of these challenges, and we particularly highlight their contribution in (a) annotating data sources and analysis methods with quality attributes to facilitate their selection given the analysis scope; (b) consistently defining study parameters such as health outcomes and drugs of interest, and providing guidance for study setup; (c) expressing analysis outcomes in a common format enabling data sharing and systematic comparisons; and (d) assessing/supporting the novelty of the aggregated outcomes through access to reference knowledge sources related to drug safety. A semantically-enriched framework can facilitate seamless access and use of different data sources and computational methods in an integrated fashion, bringing a new perspective for large-scale, knowledge intensive signal detection. Key Points A number of comparative studies assessing various signal detection methods applied to diverse types of data have highlighted the need for combinatorial-integrated approaches. Large-scale integrated signal detection requires systematic frameworks in order to address the challenges posed within the underlying concurrent analysis setting. Semantic technologies and tools may provide the means to address the challenges posed in integrated signal detection, and establish the basis for knowledge-intensive signal detection.

International audience; 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 an 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 professionals' priority).

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-P

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 diagnosis.

Automated signal generation in pharmacovigilance implements unsupervised statistical machine learning techniques in order to discover unknown adverse drug reactions (ADR) in spontaneous reporting systems. The impact of the terminology used for coding ADRs has not been addressed previously. The Medical Dictionary for Regulatory Activities (MedDRA) used worldwide in pharmacovigilance cases does not provide formal definitions of terms. We have built an ontology of ADRs to describe semantics of MedDRA terms. Ontological subsumption and approximate matching inferences allow a better grouping of medically related conditions. Signal generation performances are significantly improved but time consumption related to modelization remains very important.

Summary Objectives: To provide an editorial introduction to the 2013 IMIA Yearbook of Medical Informatics with an overview of its contents and contributors. Methods: A brief overview of the main theme, and an outline of the purposes, contents, format, and acknowledgment of contributions. Results: Health information technology (HIT) is currently widely implemented to improve healthcare quality and patient safety while reducing costs. Although these benefits are expected and largely advertised, the evidence for these benefits is still missing. Unintended consequences are often reported and some applications have been shown to be wasteful, harmful, and even fatal. Evidence-based health informatics has been defined as “the conscientious, explicit and judicious use of current best evidence when making decisions about the introduction and operation of information technology in a given health care setting”. The 2013 issue of the IMIA Yearbook highlights important contributions about the significant challenges that arise from the assessment of HIT solutions. Progress towards evidence-based health informatics is identified to elicit what works, what doesn't work, and why. In an environment where resources are limited, budgets lower than in past years, and the need to improve care is becoming ever more pressing, focusing on this topic should guide institutions and providers in the implementation of the best health information technology. Conclusion: This overview of progress and current challenges across the spectrum of the discipline shows many great examples of evidence that have been gathered on the effectiveness of HIT. However, evidence remains limited and a significant work should be conducted to improve the development, testing, and implementation of HIT applications.

Medical practice is based on the experience of practitioners and on learned medical knowledge. This knowledge is based on studies of patient's population. Modern medicine is facing a variety of clinical forms and also variable patients’ responses to treatment. Pharmacogenomics has brought insights to this variability and has led to the development of personalized medicine. The adoption of personalized medicine is slowed down by a number of technical and methodology barriers. The concept of personalized medicine should not be only limited to genetics but must reuse all patient information to get the most suitable patient profile. In this paper we present a methodology for the integration of personalized medicine into clinical practice.

SummaryObjectives: To provide an introduction to the 2016 IMIA Yearbook of Medical Informatics by the editors.Methods: We present a brief overview of the 2016 special topic “Unintended consequences of Health IT: new problems, new solutions”, we review our choice of special topic section editors, and discuss the transitions in the editorial team for next year.Results: This edition of the Yearbook acknowledges the fact that implementation and use of Health Information Technology (HIT) may result in unintended consequences, which may lead to both adverse and sometimes beneficial outcomes. However to date, in the literature, undesired outcomes are emphasized with a focus on the complex causes and the many sources that may generate them. The growing awareness of the importance of HIT’s unintended consequences and their increasing documentation reflect a wider acceptance of HIT by users (more use generating more consequences) and and a new type of users (a shift from early adopters to late adopters and laggards), whith great expectations regarding the improvement of care quality through HIT solutions. Different points of view on new problems and new solutions of unintended consequences of Health IT are presented through the keynote paper, survey papers, and the working group contributions.Conclusions: The regular 2016 issue of the IMIA yearbook focuses on new unintended consequences of Health IT – brought on by wider adoption and different types of users as well as solutions to addressing them.

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.

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.

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.

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.

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 incidence of obesity and diabetes. Methods: We have developed a framework for interacting with patient’s heterogeneous data (omics, clinical and biological 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 patients. We have a performance of 91%. Conclusion: This work represents a preliminary step in developing a CDSS and we’ll use a clinical database to test this system and to compare it with others statistic reasoning methods.

Different software engineering and artificial intelligence methods can be used to design Internet retrieval of prototypical medical images. We used the case-based reasoning (CBR) approach to provide an 'intelligent' access to a collection of illustrated medical cases through the Internet. This paper presents a Web interface for the CBR system IDEM (image and diagnosis from examples in medicine) in the domain of breast pathology. Thanks to the definition of a similarity measure between the descriptions of cases we propose a flexible querying of the case-base and a quantitative browsing among cases through similarity links. The resemblance rates provided by the system argue for the quality and the relevancy of the retrieved data. The flexibility of the querying process is robust to missing information and could be adapted to a daily practice. The CBR approach is a promising method for a clinical relevant and an efficient retrieval of reference images and diagnosis clues through Internet.

A CDSS (Clinical Decision Support System) aiming to support the exploitation of CG (Clinical Guidelines) by HCP (Health Care Practitioners) has been designed, able to consider the available knowledge about the patient’s health stored within the CIS (Clinical Information System), using the CIS native, well-trained functions and ergonomics. Amongst the main methods used, figure rule based decision trees to represent the CG knowledge, concept dictionary bonded to international standard terminological systems for semantic indexing, usage of CIS components as part of the CDSS to ensure the respect of the clinical workflow. The results obtained are threefold: 1) a CG model structure adapted for such CDSS; 2) a semantic interoperability platform populated with SNOMED 3.5 international terminology system between CDSS and Electronic Healthcare Records; 3) a workflow of clinical information systems elements coupled by a rule engine solution allowing authoring CG as decision trees. The semantic interoperability platform is up and running in more than sixty large French healthcare organizations, the CDSS is available for first exploitation experiments.

Non-alcoholic fatty liver disease (NAFLD) is a hepatic disease associated with metabolic syndrome. NAFLD covers a spectrum of liver disease from steatosis to non-alcoholic steatohepatitis (NASH) and cirrhosis. NASH is a disease evolving under the influence of various stimuli still poorly understood. In this paper we present new clinical decision support system (CDSS) for the diagnosis of NASH and the comparison of this system with machine learning algorithms.

The difficult task of detecting adverse drug events (ADEs) and the tedious process of building manual reports of ADE occurrences out of patient profiles result in a majority of adverse reactions not being reported to health regulatory authorities. The SALUS individual case safety report (ICSR) reporting tool, a component currently developed within the SALUS project, aims to support semi-automatic reporting of ADEs to regulatory authorities. In this paper, we present an initial design and current state of of our ICSR reporting tool that features: (i) automatic pre-population of reporting forms through extraction of the patient data contained in an Electronic Health Record (EHR); (ii) generation and electronic submission of the completed ICSRs by the physician to regulatory authorities; and (iii) integration of the reporting process into the physician's work-flow to limit the disturbance. The objective is to increase the rates of ADE reporting and the quality of the reported data. The SALUS interoperability platform supports patient data extraction independently of the EHR data model in use and allows generation of reports using the format expected by regulatory authorities.

Clinical data captured in hospital information systems may be unusable in their original format due to missing information or knowledge. The use of external resources (e.g. domain ontology) could be a way of dealing with this lack of knowledge. Our study thus aimed to develop a framework allowing a user to perform medical queries in the context of infectious diseases. By creating an interaction between a knowledge source and clinical data, using semantic and semantic web tools and methods, the users are able to perform queries on a database to obtain results about antibiotic resistance. This work has been performed in the context of the DebugIT European project that aims to control and monitor the antibioresistance growth via a semantic interoperability platform. The results obtained by the use of different semantic web tools were quantitatively evaluated by comparison of the number of results and the query execution time. We have compared our approach with classic business intelligence approaches in terms of usability and functionality.

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”. Background: SALUS project aims at building an interoperability platform and a dedicated toolkit to enable secondary use of electronic health records (EHR) data for post marketing drug surveillance. An important component of this toolkit is a drug-related adverse events (AE) reporting system designed to facilitate and accelerate the reporting process using automatic prepopulation mechanisms. Objective: To demonstrate SALUS approach for establishing syntactic and semantic inter-operability for AE reporting. Method: Standard (e.g. HL7 CDA-CCD) and proprietary EHR data models are mapped to the E2B(R2) data model via SALUS Common Information Model. Terminology mapping and terminology reasoning services are designed to ensure the automatic conversion of source EHR terminologies (e.g. ICD-9-CM, ICD-10, LOINC or SNOMED-CT) to the target terminology MedDRA which is expected in AE reporting forms. A validated set of terminology mappings is used to ensure the reliability of the reasoning mechanisms. Results: The percentage of data elements of a standard E2B report that can be completed automatically has been estimated for two pilot sites. In the best scenario (i.e. the avail able fields in the EHR have actually been filled), only 36% (pilot site 1) and 38% (pilot site 2) of E2B data elements remain to be filled manually. In addition, most of these data elements shall not be filled in each report. Conclusion: SALUS platform’s interopera bility solutions enable partial automation of the AE reporting process, which could con tribute to improve current spontaneous reporting practices and reduce under-report ing, which is currently one major obstacle in the process of acquisition of pharmacovigilance data.

International audience; This paper presents the activities of the theme C “medical information systems and databases” in the GDR Stic Santé. Six one-day workshops have been organized during the period 2011–2012. They were devoted to 1) sharing anatomical and physiological object models for simulation of clinical medical images, 2) advantages and limitations of datawarehouse for biological data, 3) medical information engineering, 4) systems for sharing medical images for research, 5) knowledge engineering for semantic interoperability in e-health applications, and 6) using context in health. In the future, our activities will continue with a specific interest on information systems for translational medicine and the role of electronic healthcare reports in decision-making. Workshops with other research groups will be organized in particular with the e-health research group.

Signal detection in pharmacovigilance should take into account all terms related to a medical concept rather than a single term. We built an OWL-DL file with formal definitions of MedDRA and SNOMED-CT concepts and performed two queries, Query 1 and 2, to retrieve narrow and broad terms within the Standard MedDRA Query (SMQ) related to 'anaphylactic shock' and the terms from the High Level Term (HLT) grouping related to 'anaphylaxis'. We compared values of the EB05 (EBGM) statistical test for disproportionality with 50 active ingredients randomly selected in the public version of the FDA pharmacovigilance database. Coefficient of correlation was R(2) = 1.00 between Query 1 and HLT; R(2) = 0.98 between Query 1 and SMQ narrow; R(2) = 0.89 between Query 2 and SMQ Narrow+Broad. Generating automated groupings of terms for signal detection is feasible but requires additional efforts in modeling MedDRA terms in order to improve precision and recall of these groupings.

Adverse drug events ADE occur frequently in both ambulatory and inpatient settings and often lead to hospitalization; these events occur more often in the elderly. ADE may result from medication errors at the medication process or from ADE. In this paper we present a new method to improve treatment of Community-acquired pneumonia. We use database of 86 patients to test and validate this approach.

Clinical data captured in hospital information systems may be unusable in their original format due to missing information or knowledge. The use of external resources (e.g. domain ontology) could be a way of dealing with this lack of knowledge. Our study thus aimed to develop a framework allowing a user to perform medical queries in the context of infectious diseases. By creating an interaction between a knowledge source and clinical data, using semantic and semantic web tools and methods, the users are able to perform queries on a database to obtain results about antibiotic resistance. This work has been performed in the context of the DebugIT European project that aims to control and monitor the antibioresistance growth via a semantic interoperability platform. The results obtained by the use of different semantic web tools were quantitatively evaluated by comparison of the number of results and the query execution time. We have compared our approach with classic business intelligence approaches in terms of usability and functionality.

With the development of platforms enabling the integration and use of phenome, genome, and exposome data in the context of international research, data management challenges are increasing, and scalable solutions for cross border and cross domain semantic interoperability need to be developed. Reusing routinely collected clinical data, especially, requires computable portable phenotype algorithms running across different electronic health record (EHR) products and healthcare systems. We propose a framework for describing and comparing mediation platforms enabling cross border phenotype identification within federated EHRs. This framework was used to describe the experience gained during the EHR4CR project and the evaluation of the platform developed for accessing semantically equivalent data elements across 11 European participating EHR systems from 5 countries. Developers of semantic interoperability platforms are beginning to address a core set of requirements in order to reach the goal of developing cross border semantic integration of data.

Despite the great interest of using genomics in clinical practice, many obstacles lie in the way of integrating genomic and personalized medicine into routine clinical care. Extended to genomic and personalized medicine, clinical decision support system (CDSS) could support the consistent and evidence-based application of genetic and genomic information in healthcare in many ways. In this paper we describe a CDSS designed to assist physicians for personalized care, and methodology for integration in the clinical workflow. A reasoning method for interacting heterogeneous knowledge and data is a necessity in the context of personalized medicine to achieve its potential and improve the quality, safety and efficiency of healthcare.

For solving heterogeneity and establishing semantic interoperability across the Web, matching and alignment of ontologies have been carried out based on their lexical, conceptual and structural similarities. When dealing with structural similarities, a similarity between ontology-entities can be found by detecting similarities between their structurally connected graph structures. In this paper, we present our ontology matching approach, proof-based ontology matching (POM), to find proof-based similarities between (both atomic and complex) entities from the given ontologies. POM calculates a semantic similarity between two ontology-entities, not only based on their structural similarities but also takes into account similarities between their deductively connected ancestor graph structures. We have evaluated our ontology matching approach compared to existing attempts. We also showed the significance of our work, by matching healthcare ontologies using POM and obtaining a networked ontology for treating urinary tract infections.

A major barrier to repurposing routinely collected data for clinical research is the heterogeneity of healthcare information systems. Electronic Healthcare Record for Clinical Research (EHR4CR) is a European platform designed to improve the efficiency of conducting clinical trials. In this paper, we propose an initial architecture of the EHR4CR Semantic Interoperability Framework. We used a model-driven engineering approach to build a reference HL7-based multidimensional model bound to a set of reference clinical terminologies acting as a global as view model. We then conducted an evaluation of its expressiveness for patient eligibility. The EHR4CR information model consists in one fact table dedicated to clinical statement and 4 dimensions. The EHR4CR terminology integrates reference terminologies used in patient care (e.g LOINC, ICD-10, SNOMED CT, etc). We used the Object Constraint Language (OCL) to represent patterns of eligibility criteria as constraints on the EHR4CR model to be further transformed in SQL statements executed on different clinical data warehouses.

Incorrect or improper diagnostic tests uses have important implications for health outcomes and costs. Clinical Decision Support Systems purports to optimize the use of diagnostic tests in clinical practice. The computerized medical reasoning should not only focus on existing medical knowledge but also on physician's previous experiences and new knowledge. Such medical knowledge is vague and defines uncertain relationships between facts and diagnosis, in this paper, Case Based Fuzzy Cognitive Maps (CBFCM) are proposed as an evolution of Fuzzy Cognitive Maps. They allow more complete representation of knowledge since case-based fuzzy rules are introduced to improve diagnosis decision. We have developed a framework for interacting with patient's data and formalizing knowledge from Guidelines in the domain of Urinary Tract Infection. The conducted study allowed us to test cognitive approaches for implementing Guidelines with Semantic Web tools. The advantage of this approach is to enable the sharing and reuse of knowledge from Guidelines, physicians experiences and simplify maintenance.

Personalized medicine may be considered an extension of traditional approaches to understanding and treating diseases, but with greater precision. A profile of a patient's genetic variation can guide the selection of drugs or treatment protocols that minimize harmful side effects or ensure a more successful outcome. In this paper we describe a decision support system designed to assist physicians for personalized care, and methodology for integration in the clinical workflow. A reasoning method for interacting heterogeneous knowledge and data is a necessity in the context of personalized medicine. Development of clinical decision support based semantic web for personalized patient care is to achieve its potential and improve the quality, safety and efficiency of healthcare.

In the context of PROTECT European project, we have developed an ontology of adverse drug reactions (OntoADR) based on the original MedDRA hierarchy and a query-based method to achieve automatic MedDRA terms groupings for improving pharmacovigilance signal detection. Those groupings were evaluated against standard handmade MedDRA groupings corresponding to first priority pharmacovigilance safety topics. Our results demonstrate that this automatic method allows catching most of the terms present in the reference groupings, and suggest that it could offer an important saving of time for the achievement of pharmacovigilance groupings. This paper describes the theoretical context of this work, the evaluation methodology, and presents the principal results.

International audience; Orphanet provides an international web-based knowledge portal for rare diseases including a collection of review articles. However, reviews and literature monitoring are manual. Thus, new documentation about a rare disease is a time-consuming process and automatically discovering knowledge from a large collection of texts is a crucial issue. This context represents a strong motivation to address the problem of extracting gene-rare diseases relationships from texts. In this paper, we tackle this issue with a cross-fertilization of information extraction and data mining techniques (sequential pattern mining under constraints). Experiments show the interest of the method for the documentation of rare diseases.

This paper describes an approach to build a Data Definition Ontology (DDO) in the context of full domain ontology integration with datasets in order to share and query clinical heterogeneous data repositories. We have adapted an existing semantic web tool (D2RQ) to implement a process that automatically generates the DDO from a database information model, thanks to reverse engineering and schema mapping approaches. This study has been performed in the context of the DebugIT European project (Detecting and Eliminating Bacteria UsinG Information Technology) that aims to control and monitor the bacterial growth via a semantic interoperability platform (IP). The evaluation of the process is based, first, on the accuracy of the produced DDO for different samples of database storage and second, by checking the congruency between the DDO and the D2RQ database mapping file.

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. Besides other methods, statistical algorithms are used to detect previously unknown ADRs, and it was noted that groupings of ADR terms can further improve safety signal detection. Standardised MedDRA Queries are developed to assist retrieval and evaluation of MedDRA-coded ADR reports. Dependent on the context of their application, different SMQs show varying degrees of specificity and sensitivity; some appear to be over-inclusive, some might miss relevant terms. Moreover, several important safety topics are not yet fully covered by SMQs. The objective of this work is to propose an automatic method for the creation of groupings of terms. This method is based on the application of the semantic distance between MedDRA terms. Several experiments are performed, showing a promising precision and an acceptable recall.

An important limitation of existing large-scale surveillance systems of infectious diseases is that they use mostly manual data collection processes and therefore usually deliver trends on an annual basis. The DebugIT project, funded by the 7th EU Framework Programme, provides an access to heterogeneous clinical data sets of different European hospitals. We compared the DebugIT control capabilities to the process and results provided by the French surveillance system of infectious disease (Institut de Veille Sanitaire (InVS)) and the antimicrobial resistance surveillance study of the Paul-Ehrlich-Society (PEG).

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs. Beside other methods, statistical methods are used to detect new ADRs in the framework of signal detection. Groupings of terms containing similar ADRs allow to increase the signal intensity and to improve the detection of new ADRs. SMQs have become reference groupings in the field of pharmacovigilance. They are built on the MedDRA terminology and thanks to the study of scientific literature. Even if SMQs are built manually by experts, they still show some shortcomings: they tend to be over-inclusive and thus become too sensitive, although they can also miss several relevant terms. Moreover, the spectrum of available SMQs is limited. The objective of this work is to propose an automated method for a flexible creation of groupings of terms. This method is based on exploitation of the semantic distance between MedDRA terms. In a first experience, we used ARD terms alone and obtained results with a high precision (mean 74% within the interval [49; 91]). In a second experience, we used ADR terms and their formal definitions and this worsened the results because the semantic information within definitions may be missing. We assume that more exhaustive definitions will have a positive effect on results.

Diagnosis decision making in medicine needs to take into account the patient’s clinical parameters, the context of illness and the medical knowledge of the physician to discuss and confirm a diagnosis. More and more decision support systems have to integrate different types of knowledge bases including the clinical practice guidelines. Fuzzy Cognitive Maps is a soft computing technique capable of dealing with situations including uncertain descriptions using similar procedure such as human reasoning does. FCM are limited to the representation of simple monotonic causal relations between concepts, however Rule Based Fuzzy Cognitive Maps start from a traditional fuzzy architecture with feedback in order to overcome FCM weaknesses. We used these two methods for modelling knowledge of Clinical Practice Guidelines and we used Semantic Web tools to implement them. We identified 25 clinical concepts and 13 diagnosis concepts. We defined 320 rules to implement FCM and 623 rules for RBFCM. 92% of the diagnoses proposed by the FCM and 94% of the diagnoses proposed by the RBFCM were correct.

Summary Objectives: To provide an editorial introduction into the 2014 IMIA Yearbook of Medical Informatics with an overview of the content, the new publishing scheme, and upcoming 25th anniversary. Methods: A brief overview of the 2014 special topic, Big Data -Smart Health Strategies, and an outline of the novel publishing model is provided in conjunction with a call for proposals to celebrate the 25th anniversary of the Yearbook. Results: ‘Big Data’ has become the latest buzzword in informatics and promise new approaches and interventions that can improve health, well-being, and quality of life. This edition of the Yearbook acknowledges the fact that we just started to explore the opportunities that ‘Big Data’ will bring. However, it will become apparent to the reader that its pervasive nature has invaded all aspects of biomedical informatics – some to a higher degree than others. It was our goal to provide a comprehensive view at the state of ‘Big Data’ today, explore its strengths and weaknesses, as well as its risks, discuss emerging trends, tools, and applications, and stimulate the development of the field through the aggregation of excellent survey papers and working group contributions to the topic. Conclusions: For the first time in history will the IMIA Yearbook be published in an open access online format allowing a broader readership especially in resource poor countries. For the first time, thanks to the online format, will the IMIA Yearbook be published twice in the year, with two different tracks of papers. We anticipate that the important role of the IMIA yearbook will further increase with these changes just in time for its 25th anniversary in 2016.

SNOMED CT and other biomedical vocabularies provide semantic identifiers for all kinds of linguistic expressions, many of which cannot be considered terms in a strict sense. We analyzed such "non-terms" in SNOMED CT and concluded that many of them cannot be interpreted as directly referring to objects or processes, but rather to information entities. Discussing two approaches to represent information entities, viz. the OBO Information artifact ontology (IAO) and the HL7 v3 Reference Information Model (RIM), we propose an integrative solution for representing information entities in SNOMED CT, in a way that is still compatible with RIM and the IAO and uses moderately enhanced description logics.

Building qualitative clinical decision support or monitoring based on information stored in clinical information (or EHR) systems cannot be done without assessing and controlling information quality. Numerous works have introduced methods and measures to qualify and enhance data, information models and terminologies quality. This paper introduces an approach based on an Information Quality Triangle that aims at providing a generic framework to help in characterizing quality measures and methods in the context of the integration of EHR data in a clinical datawarehouse. We have successfully experimented the proposed approach at the HEGP hospital in France, as part of the DebugIT EU FP7 project.

National audience; L'utilisation des outils et techniques du web sémantique pour intégrer, modéliser et interroger des bases de données relationnelles est un moyen de prendre en compte la dimension sémantique des données et d'en faciliter l'exploitation. Les données issues des systèmes d'information hospitaliers (SIH) peuvent êtres inutilisables dans leur format d'origine en raison de l'absence d'informations ou de connaissances. Elles nécessitent donc d'être réorganisées et liées à des ressources externes de connaissances telles que des ontologies ou terminologies. Dans le cadre de cette étude, nous utilisons des données opérationnelles du SIH de l'hôpital européen Georges Pompidou, une ontologie comme source de connaissances et des outils du web sémantique pour interroger notre base de données opérationnelle et répondre ainsi à des questions médicales dans le domaine de la résistance des bactéries aux antibiotiques. Le champ d'application de contrôle des infections des parties prenantes de cette étude est délimité par le biais de plusieurs requêtes, par exemple « la liste des patients ayant une infection spécifique (ex : infection urinaire) par un agent pathogène donné (ex : E. coli) résistante à un antibiotique donné (ex : amoxicilline) ».

In this work, we present a methodology for evaluating an ontology designed in a previous study to describe adverse drug reactions. We evaluate it in term of its fitness for grouping cases in pharmacovigilance. We define as gold standard the Standardized MedDRA Queries (SMQs) developed manually to group terms representing similar medical conditions. We perform an automatic search in the ontology in order to retrieve concepts related to the medical conditions. An optimal query is built for each medical condition. The evaluation relies on the comparison between the terms in the SMQ and the terms subsumed by the query. The result is quantified by sensitivity and specificity. We applied this methodology for 24 SMQs and we obtain a mean sensitivity of 0.82. This work allows validating the semantic resource and provides, in perspective, tools to maintain the ontology while the knowledge is evolving.

Background WHO-ART and MedDRA are medical terminologies used for the coding of adverse drug reactions in pharmacovigilance databases. MedDRA proposes 13 Special Search Categories (SSC) grouping terms associated to specific medical conditions. For instance, the SSC "Haemorrhage" includes 346 MedDRA terms among which 55 are also WHO-ART terms. WHO-ART itself does not provide such groupings. Our main contention is the possibility of classifying WHO-ART terms in semantic categories by using knowledge extracted from SNOMED CT. A previous paper presents the way WHO-ART term definitions have been automatically generated in a description logics formalism by using their corresponding SNOMED CT synonyms. Based on synonymy and relative position of WHO-ART terms in SNOMED CT, specialization or generalization relationships could be inferred. This strategy is successful for grouping the WHO-ART terms present in most MedDRA SSCs. However the strategy failed when SSC were organized on other basis than taxonomy. Methods We propose a new method that improves the previous WHO-ART structure by integrating the associative relationships included in SNOMED CT. Results The new method improves the groupings. For example, none of the 55 WHO-ART terms in the Haemorrhage SSC were matched using the previous method. With the new method, we improve the groupings and obtain 87% coverage of the Haemorrhage SSC. Conclusion SNOMED CT's terminological structure can be used to perform automated groupings in WHO-ART. This work proves that groupings already present in the MedDRA SSCs (e.g. the haemorrhage SSC) may be retrieved using classification in SNOMED CT.

The characterisation of biomedical knowledge taking into account the degree of confidence expressed in texts, is an important issue in the biomedical domain. The authors of scientific texts use grammatical and lexical devices to qualify their assertions. We named these markers of qualification "confidence markers". We present here the results of our efforts to collect confidence markers from full texts and abstracts, to classify them on the basis of semantics, and their use within our knowledge extraction system. We propose in this study, an implementation of these confidence markers for functional annotation of the human gene Apolipoprotein (APOE) thought to be involved in Alzheimer's disease. As a result, we obtain, through the extraction system, triplets: (G, F, PMID), in which G is the gene APOE, F is its function found in texts and the PMID of the article from which this knowledge was extracted. Moreover, a spatial 3D of the triplets, relative to each other, is proposed depending on their respective confidence degree.

Structured entry forms for clinical records should be updated to take into account the physicians' needs during consultation and advances in medical knowledge and practice. We updated the computerized medical record form of a hypertension clinic, based on its previous use and clinical guidelines. A statistical analysis of previously completed forms identified several unnecessary items rarely used by clinicians. A terminological analysis of guidelines and of free-text answers on completed forms identified several new topics relevant to current clinical practice. We therefore added new items to the form and some topics previously recorded as free text were itemized. We collaborated with clinicians in interpretation of the results of the statistical and terminological analyses used as the starting point and guide for this updating process.

Despite decades of work, there is no universally accepted standard medical terminology and no generally usable terminological tools have yet emerged. The local dictionary of concepts of the Georges Pompidou European Hospital (HEGP) is a Terminological System (TS) designed to support clinical data entry. It covers 93 data entry forms and contains definitions and synonyms of more than 5000 concepts, sometimes linked to reference terminologies such as ICD-10. In this article, we evaluate to which extend SNOMED CT could fully replace or rather be mapped to the local terminology system. We first describe the local dictionary of concepts of HEGP according to some published TS characterization framework. Then we discuss the specific role that a local terminology system plays with regards to reference terminologies.

The effectiveness of computerized clinical decision support systems (CDSS) depends on the quality of the knowledge they refer to. In this article, we are interested in the acquisition, modeling and representation of the knowledge embedded in the "national reference framework of drug-drug interaction" published by the French Health Products Safety Agency.A model of drug-drug interactions has been designed using bottom-up and top-down approaches. This model is the basis for the design of an XML format to represent and extract information on drug interactions from the reference framework.A specific tool has been developed to extract the information from a corpus of 1053 drug monographs using a methodology similar to the one used by the GEM-Cutter tool to extract information from clinical guidelines. Strategies to integrate the XML files produced into CDDSSs are discussed.Modeling and acquisition of drug-drug interaction knowledge from a corpus of drug monographs is a potential approach to foster the development of CDSS and improve their specificity.