Oya Deniz Beyan

An Ontology for Clinical Trial Data Integration

A set of well-integrated clinical terminologies is at the core of delivering an efficient clinical trial system. The design and outcomes of a clinical trial can be improved significantly through an unambiguous and consistent set of clinical terminologies used in a participating clinical institute. However, due to lack of generalised legal and technical standards, heterogeneity exists between prominent clinical terminologies as well as within and between clinical systems at several levels, e.g., data, schema, and medical codes. This article specifically addresses the problem of integrating local or proprietary clinical terminologies with the globally defined universal concepts or terminologies. To deal with the problem of ambiguous, inconsistent, and overlapping clinical terminologies, domain and knowledge representation specialists have been repeatedly advocated the use of formal ontologies. We address two key challenges in developing an ontology-based clinical terminology (1) an ontology building methodology for clinical terminologies that are separated in global and local layers, and (2) aligning global and local clinical terminologies. We present Semantic Electronic Health Record (SEHR) ontology that covers multiple sub-domains of Healthcare and Life Sciences (HCLS) through specialisation of the upper-level Basic Formal Ontology (BFO). One of the main features of SEHR is layering and adaptation of local clinical terminologies with the upper-level BFO. Our empirical evaluation shows an agreement of clinical experts confirming SEHRs usability in clinical trials.

Robot-assisted care for elderly with dementia: is there a potential for genuine end-user empowerment?

In this paper, we describe considerations arising in relation to the achievement of an ethical design process for an assistive care robot within the H2020 project MARIO. Envisaged end-users of the robot are elderly with mild to moderate dementia in residential care and community settings. MARIO aims to achieve a value sensitive design process with significant end-user involvement in the design of the robot, eliciting their preferences regarding desirable functionalities and identifying ethical concerns. The realization of this participatory approach with persons with dementia raises a number of ethical challenges that the project aims to address.

Implementing an ethical approach to big data analytics in assistive robotics for elderly with dementia

In this paper, we analyse the ethical relevance of emerging informational aspects in robotics for the area of care robotics. We identify specific informational characteristics of contemporary and emerging robots, especially the fact of their increasing informational connectedness. We then outline specific ethical considerations arising in the design process in the H2020 project MARIO which aims to develop a care robot for persons with mild to moderate dementia in home and residential care settings. Ethical considerations regarding specific functionalities of the proposed care robot are outlined.

Advancing Patient Record Safety and EHR Semantic Interoperability

Electronic Health Records (EHRs) contain an increasing wealth of medical information, which has the potential to significantly advance medical research and health policies formulation, providing society with additional benefits within a global health perspective. However, the European healthcare information space is fragmented due to the lack of legal and technical standards, cost effective platforms, and sustainable business models. Providing an interoperability infrastructure for EHRs is on the agenda of many regional, and international eHealth initiatives. The semantic interoperability of patient data between EHRs and medical research can transform todays process of drug discovery and development, enable faster access to effective new medications, provide improved patient outcomes, and provide a key foundation for targeted (personalized) medicines. The scope of the current paper is the description of the effort undertaken by the Linked2Safety consortium towards the development of an innovative interoperability framework, for the efficient, homogenized access to and the effective utilization of the increasing wealth of medical information contained in the EHR systems deployed and maintained at regional and/or national level across Europe.

Smart Medical Information Technology for Healthcare (SMITH)

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. “Smart Medical Information Technology for Healthcare (SMITH)” is one of four consortia funded by the German Medical Informatics Initiative (MI-I) to create an alliance of universities, university hospitals, research institutions and IT companies. SMITH’s goals are to establish Data Integration Centers (DICs) at each SMITH partner hospital and to implement use cases which demonstrate the usefulness of the approach. Objectives: To give insight into architectural design issues underlying SMITH data integration and to introduce the use cases to be implemented. Governance and Policies: SMITH implements a federated approach as well for its governance structure as for its information system architecture. SMITH has designed a generic concept for its data integration centers. They share identical services and functionalities to take best advantage of the interoperability architectures and of the data use and access process planned. The DICs provide access to the local hospitals’ Electronic Medical Records (EMR). This is based on data trustee and privacy management services. DIC staff will curate and amend EMR data in the Health Data Storage. Methodology and Architectural Framework: To share medical and research data, SMITH’s information system is based on communication and storage standards. We use the Reference Model of the Open Archival Information System and will consistently implement profiles of Integrating the Health Care Enterprise (IHE) and Health Level Seven (HL7) standards. Standard terminologies will be applied. The SMITH Market Place will be used for devising agreements on data access and distribution. 3LGM 2 for enterprise architecture modeling supports a consistent development process. The DIC reference architecture determines the services, applications and the standards-based communication links needed for efficiently supporting the ingesting, data nourishing, trustee, privacy management and data transfer tasks of the SMITH DICs. The reference architecture is adopted at the local sites. Data sharing services and the market place enable interoperability. Use Cases: The methodological use case “Phenotype Pipeline” (PheP) constructs algorithms for annotations and analyses of patient-related phenotypes according to classification rules or statistical models based on structured data. Unstructured textual data will be subject to natural language processing to permit integration into the phenotyping algorithms. The clinical use case “Algorithmic Surveillance of ICU Patients” (ASIC) focusses on patients in Intensive Care Units (ICU) with the acute respiratory distress syndrome (ARDS). A model-based decision-support system will give advice for mechanical ventilation. The clinical use case HELP develops a “hospital-wide electronic medical record-based computerized decision support system to improve outcomes of patients with blood-stream infections” (HELP). ASIC and HELP use the PheP. The clinical benefit of the use cases ASIC and HELP will be demonstrated in a change of care clinical trial based on a step wedge design. Discussion: SMITH’s strength is the modular, reusable IT architecture based on interoperability standards, the integration of the hospitals’ information management departments and the public-private partnership. The project aims at sustainability beyond the first 4-year funding period.

A semantic architecture for preserving and interpreting the information contained in Irish historical vital records

Irish Record Linkage 1864–1913 is a multi-disciplinary project that started in 2014 aiming to create a platform for analyzing events captured in historical birth, marriage, and death records by applying semantic technologies for annotating, storing, and inferring information from the data contained in those records. This enables researchers to, among other things, investigate to what extent maternal and infant mortality rates were underreported. We report on the semantic architecture, provide motivation for the adoption of RDF and Linked Data principles, and elaborate on the ontology construction process that was influenced by both the requirements of the digital archivists and historians. Concerns of digital archivists include the preservation of the archival record and following best practices in preservation, cataloguing, and data protection. The historians in this project wish to discover certain patterns in those vital records. An important aspect of the semantic architecture is the clear separation of concerns that reflects those distinct requirements—the transcription and archival authenticity of the register pages and the interpretation of the transcribed data—that led to the creation of two distinct ontologies and knowledge bases. The advantage of this clear separation is the transcription of register pages resulted in a reusable data set fit for other research purposes. These transcriptions were enriched with metadata according to best practices in archiving for ingestion in suitable long-term digital preservation platforms.

On a Linked Data Platform for Irish Historical Vital Records

The Irish Record Linkage 1864–1913 is a multi-disciplinary project aiming to create a platform for analyzing events captured in historical birth, marriage and death records by applying semantic technologies for annotating, storing and inferring information from the data contained in those records. This enables researchers to, for instance, investigate to what extent maternal and infant mortality rates were underreported. We report on the semantic architecture, provide motivation for the adoption of RDF and Linked Data principles, and elaborate on the ontology construction process that was influenced by both the requirements of the digital archivists and historians. Concerns of digital archivists include the preservation of the archival record and following best practices in preservation, cataloguing and data protection. The historians in this project wish to discover certain patterns in those vital records. An important aspect of the semantic architecture is the clear separation of concerns that reflects those requirements – the transcription and archival authenticity of the register pages and the interpretation of the transcribed data – that led to the creation of two distinct ontologies and knowledge bases.

Querying phenotype-genotype associations across multiple knowledge bases using Semantic Web technologies

Biomedical and genomic data are inherently heterogeneous and their recent proliferation over the Web has demanded innovative querying methods to help domain experts in their clinical and research studies. In this paper we present the use of Semantic Web technologies in querying diverse phenotype-genotype associations for supporting personalized medicine and potentially helping to discover new associations. Our initial results suggest that Semantic Web technologies has competitive advantages in extracting, consolidating and presenting phenotype-genotype associations that resides in various bioinformatics resources. The developed querying method could support researchers and medical professionals in discovering and utilizing information on published associations relating disease, treatment, adverse events and environmental factors to genetic markers from multiple repositories.

A Framework for Applying Data Integration and Curation Pipelines to Support Integration of Migrants and Refugees in Europe

We investigate the benefit of data integration and curation services for the current refugee crisis and proposed an architecture to support development of innovative solutions. We focus on developing a multi- /cross-lingual semantic data curation pipeline enriched with natural language processing capabilities in order to (a) improve decision making capabilities of public authorities with data driven dashboards; (b) stimulate the development of innovative application and services supporting integration of refugees; and (c) improve the use of open data for tackling the societal challenges.

Historical Data Preservation and Interpretation Pipeline for Irish Civil Registration Records

Semantic Web technologies give us the opportunity to understand today’s data-rich society and provide novel means to explore our past. Civil registration records such as birth, death, and marriage registers contain a vast amount of implicit information which can be revealed by structuring, linking and combining that information with other datasets and bodies of knowledge. In the Irish Record Linkage (IRL) Project 1864-1913, we have developed a data preservation and interpretation pipeline supported by a dedicated semantic architecture. This three-layered pipeline is designed to capture separate concerns from the perspective of multiple disciplines such as archival studies, history and data science. In this study, our aim is to demonstrate best practices in digital archives, while facilitating innovative new methodologies in historical research. The designed pipeline is executed with a dataset of 4090 registered Irish death entries from selected areas of south Dublin City.