To improve patient outcomes after trauma, the need to decrypt the post-traumatic immune response has been identified. One prerequisite to drive advancement in understanding that domain is the implementation of surgical biobanks. This paper focuses on the outcomes of patients with one of two diagnoses: post-traumatic arthritis and osteomyelitis. In creating surgical biobanks, currently, many obstacles must be overcome. Roadblocks exist around scoping of data that is to be collected, and the semantic integration of these data. In this paper, the generic component model and the Semantic Web technology stack are used to solve issues related to data integration. The results are twofold: (a) a scoping analysis of data and the ontologies required to harmonize and integrate it, and (b) resolution of common data integration issues in integrating data relevant to trauma surgery.
- Keywords
- biomedical ontologies, knowledge representation, osteomyelitis, post-traumatic arthritis, semantic data integration, surgical biobank, system theory,
- Publication type
- Journal Article MeSH
Electronic Health Record (EHR) systems currently in use are not designed for widely interoperable longitudinal health data. Therefore, EHR data cannot be properly shared, managed and analyzed. In this article, we propose two approaches to making EHR data more comprehensive and FAIR (Findable, Accessible, Interoperable, and Reusable) and thus more useful for diagnosis and clinical research. Firstly, the data modeling based on the LinkML framework makes the data interoperability more realistic in diverse environments with various experts involved. We show the first results of how diverse health data can be integrated based on an easy-to-understand data model and without loss of available clinical knowledge. Secondly, decentralizing EHRs contributes to the higher availability of comprehensive and consistent EHR data. We propose a technology stack for decentralized EHRs and the reasons behind this proposal. Moreover, the two proposed approaches empower patients because their EHR data can become more available, understandable, and usable for them, and they can share their data according to their needs and preferences. Finally, we explore how the users of the proposed solution could be involved in the process of its validation and adoption.
- Keywords
- Distributed electronic health records, FAIR principles, HL7 FHIR, bio-data management, ontology,
- MeSH
- Data Management MeSH
- Electronic Health Records * MeSH
- Humans MeSH
- Semantic Web * MeSH
- Software MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
UNLABELLED: With biodiversity research activities being increasingly shifted to the web, the need for a system of persistent and stable identifiers for physical collection objects becomes increasingly pressing. The Consortium of European Taxonomic Facilities agreed on a common system of HTTP-URI-based stable identifiers which is now rolled out to its member organizations. The system follows Linked Open Data principles and implements redirection mechanisms to human-readable and machine-readable representations of specimens facilitating seamless integration into the growing semantic web. The implementation of stable identifiers across collection organizations is supported with open source provider software scripts, best practices documentations and recommendations for RDF metadata elements facilitating harmonized access to collection information in web portals. DATABASE URL: : http://cetaf.org/cetaf-stable-identifiers.
OBJECTIVES: The data interchange in the Czech healthcare environment is mostly based on national standards. This paper describes a utilization of international standards and nomenclatures for building a pilot semantic interoperability platform (SIP) that would serve to exchange information among electronic health record systems (EHR-Ss) in Czech healthcare. The work was performed by the national research project of the "Information Society" program. METHODS: At the beginning of the project a set of requirements the SIP should meet was formulated. Several communication standards (openEHR, HL7 v3, DICOM) were analyzed and HL7 v3 was selected to exchange health records in our solution. Two systems were included in our pilot environment: WinMedicalc 2000 and ADAMEKj EHR. RESULTS: HL7-based local information models were created to describe the information content of both systems. The concepts from our original information models were mapped to coding systems supported by HL7 (LOINC, SNOMED CT and ICD-10) and the data exchange via HL7 v3 messages was implemented and tested by querying patient administration data. As a gateway between local EHR systems and the HL7 message-based infrastructure, a configurable HL7 Broker was developed. CONCLUSIONS: A nationwide implementation of a full-scale SIP based on HL7 v3 would include adopting and translating appropriate international coding systems and nomenclatures, and developing implementation guidelines facilitating the migration from national standards to international ones. Our pilot study showed that our approach is feasible but it would demand a huge effort to fully integrate the Czech healthcare system into the European e-health context.
The article deals with and discusses two main approaches in building semantic structures for electrophysiological metadata. It is the use of conventional data structures, repositories, and programming languages on one hand and the use of formal representations of ontologies, known from knowledge representation, such as description logics or semantic web languages on the other hand. Although knowledge engineering offers languages supporting richer semantic means of expression and technological advanced approaches, conventional data structures and repositories are still popular among developers, administrators and users because of their simplicity, overall intelligibility, and lower demands on technical equipment. The choice of conventional data resources and repositories, however, raises the question of how and where to add semantics that cannot be naturally expressed using them. As one of the possible solutions, this semantics can be added into the structures of the programming language that accesses and processes the underlying data. To support this idea we introduced a software prototype that enables its users to add semantically richer expressions into a Java object-oriented code. This approach does not burden users with additional demands on programming environment since reflective Java annotations were used as an entry for these expressions. Moreover, additional semantics need not to be written by the programmer directly to the code, but it can be collected from non-programmers using a graphic user interface. The mapping that allows the transformation of the semantically enriched Java code into the Semantic Web language OWL was proposed and implemented in a library named the Semantic Framework. This approach was validated by the integration of the Semantic Framework in the EEG/ERP Portal and by the subsequent registration of the EEG/ERP Portal in the Neuroscience Information Framework.
- Keywords
- EEG/ERP portal, electrophysiology, object-oriented code, ontology, semantic framework, semantic web,
- Publication type
- Journal Article MeSH
INTRODUCTION: Sample collections and data are hosted within different biobanks at diverse institutions across Europe. Our data integration framework aims at incorporating data about sample collections from different biobanks into a common research infrastructure, facilitating researchers' abilities to obtain high-quality samples to conduct their research. The resulting information must be locally gathered and distributed to searchable higher level information biobank directories to maximize the visibility on the national and European levels. Therefore, biobanks and sample collections must be clearly described and unambiguously identified. We describe how to tackle the challenges of integrating biobank-related data between biobank directories using heterogeneous data schemas and different technical environments. METHODS: To establish a data exchange infrastructure between all biobank directories involved, we propose the following steps: (A) identification of core entities, terminology, and semantic relationships, (B) harmonization of heterogeneous data schemas of different Biobanking and Biomolecular Resources Research Infrastructure (BBMRI) directories, and (C) formulation of technical core principles for biobank data exchange between directories. RESULTS: (A) We identified the major core elements to describe biobanks in biobank directories. Since all directory data models were partially based on Minimum Information About BIobank Data Sharing (MIABIS) 2.0, the MIABIS 2.0 core model was used for compatibility. (B) Different projection scenarios were elaborated in collaboration with all BBMRI.at partners. A minimum set of mandatory and optional core entities and data items was defined for mapping across all directory levels. (C) Major core data exchange principles were formulated and data interfaces implemented by all biobank directories involved. DISCUSSION: We agreed on a MIABIS 2.0-based core set of harmonized biobank attributes and established a list of data exchange core principles for integrating biobank directories on different levels. This generic approach and the data exchange core principles proposed herein can also be applied in related tasks like integration and harmonization of biobank data on the individual sample and patient levels.
- Keywords
- BBMRI, EDI interface, MIABIS, RESTful, biobank directory, data integration,
- MeSH
- Biological Specimen Banks * MeSH
- Humans MeSH
- Specimen Handling methods MeSH
- Information Dissemination methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Austria MeSH
This paper deals with a developed information system called a Personal Genetic Card (PGC). The system aims to integrate the known clinical knowledge (interpretations and recommendations) linked to genetic information with the analysis results of a patient. Genetic information has an increasing influence on the clinical decision of physicians as well as other medical and health services. All these services need to connect the genetic profile with the phenotypes such as drug metabolization, drug toxicity, drug dosing, or intolerance of some substances. It still applies that the best way to represent data of medical records is a structured form of record. Many approaches can be used to define the structure (syntax) of the record and the content (semantics) of the record and to exchange data in forms of various standards and terminologies. Moreover, the genetic analysis field has its terminology databases for representing genetic information (e.g. HGNC, NCBI). The next step is to connect the genetic analysis results with c clinical knowledge (interpretation, recommendation). This step is crucial because the genetic analysis results have clinical benefits if we can assign them to some valid clinical knowledge. And the best final result is when we can make a better recommendation based on the genetic results and clinical knowledge. Genetic knowledge databases (e.g. PharmGKB, SNPedia, ClinVar) contain many interpretations and even recommendations for genetic analysis results based on different purposes. This situation is appropriate for developing the PGC system that takes inspiration from case-based reasoning in purpose to allow integration of the assumptions and knowledge about phenotypes and the real genetic analysis results in the structured form.
- Keywords
- Information system, eHealth, genetic analysis, standards, terminology,
- MeSH
- Medical Records Systems, Computerized * MeSH
- Phenotype MeSH
- Genetic Testing * MeSH
- Semantics MeSH
- Publication type
- Journal Article MeSH
After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others, achieved remarkable compression ratios and encoding and decoding speeds with acceptable data quality, which has kept them in common use to this day. However, recent computing paradigms such as cloud computing, edge computing, the Internet of Things (IoT), and digital preservation have gradually posed new challenges, and, as a consequence, development trends in data compression are focusing on concepts that were not previously in the spotlight. In this article, we try to critically evaluate the most prominent of these trends and to explore their parallels, complementarities, and differences. Digital data restoration mimics the human ability to omit memorising information that is satisfactorily retrievable from the context. Feature-based data compression introduces a two-level data representation with higher-level semantic features and with residuals that correct the feature-restored (predicted) data. The integration of the advantages of individual domain-specific data compression methods into a general approach is also challenging. To the best of our knowledge, a method that addresses all these trends does not exist yet. Our methodology, COMPROMISE, has been developed exactly to make as many solutions to these challenges as possible inter-operable. It incorporates features and digital restoration. Furthermore, it is largely domain-independent (general), asymmetric, and universal. The latter refers to the ability to compress data in a common framework in a lossy, lossless, and near-lossless mode. COMPROMISE may also be considered an umbrella that links many existing domain-dependent and independent methods, supports hybrid lossless-lossy techniques, and encourages the development of new data compression algorithms.
- Keywords
- data compression, data restoration, feature, residual, universal algorithm,
- Publication type
- Journal Article MeSH
The paper describes several classification systems that could improve patient safety through semantic interoperability among contemporary electronic health record systems (EHR-Ss) with support of the HL7 v3 standard. We describe a proposal and a pilot implementation of a semantic interoperability platform (SIP) interconnecting current EHR-Ss by using HL7 v3 messages and concepts mappings on most widely used classification systems. The increasing number of classification systems and nomenclatures requires designing of various conversion tools for transfer between main classification systems. We present the so-called LIM filler module and the HL7 broker, which are parts of the SIP, playing the role of such conversion tools. The analysis of suitability and usability of individual terminological thesauri has been started by mapping of clinical contents of the Minimal Data Model for Cardiology (MDMC) to various terminological classification systems. A national-wide implementation of the SIP would include adopting and translating international coding systems and nomenclatures, and developing implementation guidelines facilitating the migration from national standards to international ones. Our research showed that creation of such a platform is feasible; however, it will require a huge effort to adapt fully the Czech healthcare system to the European environment.
- MeSH
- Medical Record Linkage methods MeSH
- Electronic Health Records organization & administration MeSH
- Cardiology methods organization & administration MeSH
- Cardiovascular Diseases classification MeSH
- Humans MeSH
- Programming Languages MeSH
- Vocabulary, Controlled * MeSH
- Semantics MeSH
- Information Dissemination methods MeSH
- Systems Integration * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Czech Republic MeSH
AI development in biotechnology relies on high-quality data to train and validate algorithms. The FAIR principles (Findable, Accessible, Interoperable, and Reusable) and regulatory frameworks such as the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR) specify requirements on specimen and data provenance to ensure the quality and traceability of data used in AI development. In this paper, a framework is presented for recording and publishing provenance information to meet these requirements. The framework is based on the use of standardized models and protocols, such as the W3C PROV model and the ISO 23494 series, to capture and record provenance information at various stages of the data generation and analysis process. The framework and use case illustrate the role of provenance information in supporting the development of high-quality AI algorithms in biotechnology. Finally, the principles of the framework are illustrated in a simple computational pathology use case, showing how specimen and data provenance can be used in the development and documentation of an AI algorithm. The use case demonstrates the importance of managing and integrating distributed provenance information and highlights the complex task of considering factors such as semantic interoperability, confidentiality, and the verification of authenticity and integrity.
- Keywords
- Artificial intelligence, Biological material, Provenance, Traceability,
- MeSH
- Algorithms * MeSH
- Biotechnology * MeSH
- Artificial Intelligence MeSH
- Publication type
- Journal Article MeSH
