'SNOMEDizing' Questionnaires for Standardizing Stroke Registry Data.

International interoperability of healthcare and research data requires a commitment to standards. To this end, SNOMED CT was evaluated for representing questionnaire items of the European Registry of Stroke Care Quality using a complex annotation protocol. The agreement between validators and annotators was 72.

A knowledge graph-based data harmonization framework for secondary data reuse.

Background And Objective: The adoption of new technologies in clinical care systems has propitiated the availability of a great amount of valuable data. However, this data is usually heterogeneous, requiring its harmonization to be integrated and analysed. We propose a semantic-driven harmonization framework that (1) enables the meaningful sharing and integration of healthcare data across institutions and (2) facilitates the analysis and exploitation of the shared data.

Improving the Quality and Utility of Electronic Health Record Data through Ontologies.

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research.

Supporting SNOMED CT postcoordination with knowledge graph embeddings.

SNOMED CT postcoordination is an underused mechanism that can help to implement advanced systems for the automatic extraction and encoding of clinical information from text. It allows defining non-existing SNOMED CT concepts by their relationships with existing ones. Manually building postcoordinated expressions is a difficult task.

Towards a Semantic Data Harmonization Federated Infrastructure.

Data integration is an increasing need in medical informatics projects like the EU Precise4Q project, in which multidisciplinary semantically and syntactically heterogeneous data across several institutions needs to be integrated. Besides, data sharing agreements often allow a virtual data integration only, because data cannot leave the source repository. We propose a data harmonization infrastructure in which data is virtually integrated by sharing a semantically rich common data representation that allows their homogeneous querying.

Clinical Text Mining on FHIR.

Semantic standards and human language technologies are key enablers for semantic interoperability across heterogeneous document and data collections in clinical information systems. Data provenance is awarded increasing attention, and it is especially critical where clinical data are automatically extracted from original documents, e.g.

Qualitative analysis of manual annotations of clinical text with SNOMED CT.

SNOMED CT provides about 300,000 codes with fine-grained concept definitions to support interoperability of health data. Coding clinical texts with medical terminologies it is not a trivial task and is prone to disagreements between coders. We conducted a qualitative analysis to identify sources of disagreements on an annotation experiment which used a subset of SNOMED CT with some restrictions.

Validating EHR clinical models using ontology patterns.

Clinical models are artefacts that specify how information is structured in electronic health records (EHRs). However, the makeup of clinical models is not guided by any formal constraint beyond a semantically vague information model. We address this gap by advocating ontology design patterns as a mechanism that makes the semantics of clinical models explicit.

Semantic Technologies for Re-Use of Clinical Routine Data.

Routine patient data in electronic patient records are only partly structured, and an even smaller segment is coded, mainly for administrative purposes. Large parts are only available as free text. Transforming this content into a structured and semantically explicit form is a prerequisite for querying and information extraction.

HL7 FHIR: Ontological Reinterpretation of Medication Resources.

"A solid ontology-based analysis with a rigorous formal mapping for correctness" is one of the ten reasons why the HL7 standard Fast Healthcare Interoperability Resources (FHIR) is advertised to be better than other standards for EHR interoperability. In this paper, we aim at contributing to this formal analysis by proposing an RDF representation of a subset of FHIR resources based on a highly constrained top-level ontology and guided by the use of a set of Content Ontology Design Patterns (Content ODPs) for representing clinical information. We exemplify this by reinterpreting FHIR medication resources.

Building SNOMED CT Post-Coordinated Expressions from Annotation Groups.

SNOMED CT supports post-coordination, a technique to combine clinical concepts to ontologically define more complex concepts. This technique follows the validity restrictions defined in the SNOMED CT Concept Model. Pre-coordinated expressions are compositional expressions already in SNOMED CT, whereas post-coordinated expressions extend its content.

The BioTop Family of Upper Level Ontological Resources for Biomedicine.

BioTop is a domain upper level ontology for the life sciences, based on OWL DL, introduced ten years ago. This paper provides an update of the current state of this resource, with a special focus on BioTop's top level, BioTopLite, which currently contains 55 classes, 37 object properties and 247 description logics axioms. A bridging file allows harmonising BioTopLite with the classes of Basic Formal Ontology BFO2.

Harmonizing SNOMED CT with BioTopLite: An Exercise in Principled Ontology Alignment.

The integration of heterogeneous ontologies is often hampered by different upper level categories and relations. We report on an on-going effort to align clinical terminology/ontology SNOMED CT with the formal upper-level ontology BioTopLite. This alignment introduces several constraints at the OWL-DL level.

Acquiring Plausible Predications from MEDLINE by Clustering MeSH Annotations.

The massive accumulation of biomedical knowledge is reflected by the growth of the literature database MEDLINE with over 23 million bibliographic records. All records are manually indexed by MeSH descriptors, many of them refined by MeSH subheadings. We use subheading information to cluster types of MeSH descriptor co-occurrences in MEDLINE by processing co-occurrence information provided by the UMLS.

Semantic enrichment of clinical models towards semantic interoperability. The heart failure summary use case.

Objective: To improve semantic interoperability of electronic health records (EHRs) by ontology-based mediation across syntactically heterogeneous representations of the same or similar clinical information.

Materials And Methods: Our approach is based on a semantic layer that consists of: (1) a set of ontologies supported by (2) a set of semantic patterns. The first aspect of the semantic layer helps standardize the clinical information modeling task and the second shields modelers from the complexity of ontology modeling.

Ontology patterns-based transformation of clinical information.

The semantic interoperability of clinical information requires methods able to transform heterogeneous data sources from both technological and structural perspectives, into representations that facilitate the sharing of meaning. The SemanticHealthNet (SHN) project proposes using semantic content patterns for representing clinical information based on a model of meaning, preventing users from a deep knowledge on ontology and description logics formalism. In this work we propose a flexible transformation method that uses semantic content patterns to guide the mapping between the source data and a target domain ontology.

Improving EHR semantic interoperability: future vision and challenges.

We propose a semantic-driven architecture to improve EHR semantic interoperability. This architecture is constituted by five layers: structured heterogeneous data (i), as found in (un-)standardised clinical information models, which are consumed by a semantic mapping layer (ii), which links the data items to clinical ontologies via user-friendly content patterns. A semantic mediator (iii) then translates these content patterns into ontology-based annotations, which populate a virtual homogeneous data store (iv), which serves the application layer (v).

Isosemantic rendering of clinical information using formal ontologies and RDF.

The generation of a semantic clinical infostructure requires linking ontologies, clinical models and terminologies [1]. Here we describe an approach that would permit data coming from different sources and represented in different standards to be queried in a homogeneous and integrated way. Our assumption is that data providers should be able to agree and share the meaning of the data they want to exchange and to exploit.

Recommendation of standardized health learning contents using archetypes and semantic web technologies.

Linking Electronic Healthcare Records (EHR) content to educational materials has been considered a key international recommendation to enable clinical engagement and to promote patient safety. This would suggest citizens to access reliable information available on the web and to guide them properly. In this paper, we describe an approach in that direction, based on the use of dual model EHR standards and standardized educational contents.

Using the ResearchEHR platform to facilitate the practical application of the EHR standards.

Possibly the most important requirement to support co-operative work among health professionals and institutions is the ability of sharing EHRs in a meaningful way, and it is widely acknowledged that standardization of data and concepts is a prerequisite to achieve semantic interoperability in any domain. Different international organizations are working on the definition of EHR architectures but the lack of tools that implement them hinders their broad adoption. In this paper we present ResearchEHR, a software platform whose objective is to facilitate the practical application of EHR standards as a way of reaching the desired semantic interoperability.

A generative tool for building health applications driven by ISO 13606 archetypes.

The use of Electronic Healthcare Records (EHR) standards in the development of healthcare applications is crucial for achieving the semantic interoperability of clinical information. Advanced EHR standards make use of the dual model architecture, which provides a solution for clinical interoperability based on the separation of the information and knowledge. However, the impact of such standards is biased by the limited availability of tools that facilitate their usage and practical implementation.

Clinical data interoperability based on archetype transformation.

The semantic interoperability between health information systems is a major challenge to improve the quality of clinical practice and patient safety. In recent years many projects have faced this problem and provided solutions based on specific standards and technologies in order to satisfy the needs of a particular scenario. Most of such solutions cannot be easily adapted to new scenarios, thus more global solutions are needed.

An approach for the semantic interoperability of ISO EN 13606 and OpenEHR archetypes.

The communication between health information systems of hospitals and primary care organizations is currently an important challenge to improve the quality of clinical practice and patient safety. However, clinical information is usually distributed among several independent systems that may be syntactically or semantically incompatible. This fact prevents healthcare professionals from accessing clinical information of patients in an understandable and normalized way.

ResearchEHR: use of semantic web technologies and archetypes for the description of EHRs.

In this paper, we present the ResearchEHR project. It focuses on the usability of Electronic Health Record (EHR) sources and EHR standards for building advanced clinical systems. The aim is to support healthcare professional, institutions and authorities by providing a set of generic methods and tools for the capture, standardization, integration, description and dissemination of health related information.

Towards ISO 13606 and openEHR archetype-based semantic interoperability.

Semantic interoperability of clinical standards is a major challenge in eHealth across Europe. It would allow healthcare professionals to manage the complete electronic healthcare record of the patient regardless of which institution generated each clinical session. Clinical archetypes are fundamental for the consecution of semantic interoperability, but they are built for particular electronic healthcare record standards.