Processes like the care of type 2 diabetes mellitus patients require support by information systems considering the heterogeneity of the actors from different domains involved, enabling harmonization and integration of their specific methodologies and knowledge representation approaches towards interdisciplinary cooperation. Currently, the development of systems starts from the simplified information world, ignoring the aforementioned heterogeneity and specificity of real-world processes. This paper aims to demonstrate the feasibility of developing an adaptive, interoperable and intelligent system that supports the major aspects of type 2 diabetes mellitus care based on the Generic Component Model as formal methodology for modelling universal systems. The result is a deployable solution based on a formal representation of the diabetes care system, its objectives, and the intended business process. The implemented system enables reasoning over the data, inferring medical diagnosis. The effectiveness of the inference was evaluated, obtaining an F-measure of 0.89. The methods presented in this paper helps to build high quality models based on computation-independent aspects, which enable the construction of knowledge-based adaptive, intelligent and interoperable eHealth systems.

• Klíčová slova
• Diabetes mellitus, Health information management, Knowledge-based systems, Ontology-based Systems, Systems architecture,
• MeSH
• diabetes mellitus 2. typu * MeSH
• informační systémy MeSH
• lidé MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Identification of non-trivial and meaningful patterns in omics data is one of the most important biological tasks. The patterns help to better understand biological systems and interpret experimental outcomes. A well-established method serving to explain such biological data is Gene Set Enrichment Analysis. However, this type of analysis is restricted to a specific type of evaluation. Abstracting from details, the analyst provides a sorted list of genes and ontological annotations of the individual genes; the method outputs a subset of ontological terms enriched in the gene list. Here, in contrary to enrichment analysis, we introduce a new tool/framework that allows for the induction of more complex patterns of 2-dimensional binary omics data. This extension allows to discover and describe semantically coherent biclusters. RESULTS: We present a new rapid method called sem1R that reveals interpretable hidden rules in omics data. These rules capture semantic differences between two classes: a target class as a collection of positive examples and a non-target class containing negative examples. The method is inspired by the CN2 rule learner and introduces a new refinement operator that exploits prior knowledge in the form of ontologies. In our work this knowledge serves to create accurate and interpretable rules. The novel refinement operator uses two reduction procedures: Redundant Generalization and Redundant Non-potential, both of which help to dramatically prune the rule space and consequently, speed-up the entire process of rule induction in comparison with the traditional refinement operator as is presented in CN2. CONCLUSIONS: Efficiency and effectivity of the novel refinement operator were tested on three real different gene expression datasets. Concretely, the Dresden Ovary Dataset, DISC, and m2816 were employed. The experiments show that the ontology-based refinement operator speeds-up the pattern induction drastically. The algorithm is written in C++ and is published as an R package available at http://github.com/fmalinka/sem1r.

• Klíčová slova
• Biclustering, Enrichment analysis, Gene expression, Ontology, Symbolic machine learning, Taxonomy,
• Publikační typ
• časopisecké články MeSH

The wide-spread use of Common Data Models and information models in biomedical informatics encourages assumptions that those models could provide the entirety of what is needed for knowledge representation purposes. Based on the lack of computable semantics in frequently used Common Data Models, there appears to be a gap between knowledge representation requirements and these models. In this use-case oriented approach, we explore how a system-theoretic, architecture-centric, ontology-based methodology can help to better understand this gap. We show how using the Generic Component Model helps to analyze the data management system in a way that allows accounting for data management procedures inside the system and knowledge representation of the real world at the same time.

• Klíčová slova
• Biomedical Ontologies, Information Models, Knowledge Representation, Systems Theory, eHealth,
• MeSH
• bio-ontologie * MeSH
• data management MeSH
• sémantika * MeSH
• Publikační typ
• časopisecké články MeSH

OBJECTIVE: For realizing pervasive and ubiquitous health and social care services in a safe and high quality as well as efficient and effective way, health and social care systems have to meet new organizational, methodological, and technological paradigms. The resulting ecosystems are highly complex, highly distributed, and highly dynamic, following inter-organizational and even international approaches. Even though based on international, but domain-specific models and standards, achieving interoperability between such systems integrating multiple domains managed by multiple disciplines and their individually skilled actors is cumbersome. METHODS: Using the abstract presentation of any system by the universal type theory as well as universal logics and combining the resulting Barendregt Cube with parameters and the engineering approach of cognitive theories, systems theory, and good modeling best practices, this study argues for a generic reference architecture model moderating between the different perspectives and disciplines involved provide on that system. To represent architectural elements consistently, an aligned system of ontologies is used. RESULTS: The system-oriented, architecture-centric, and ontology-based generic reference model allows for re-engineering the existing and emerging knowledge representations, models, and standards, also considering the real-world business processes and the related development process of supporting IT systems for the sake of comprehensive systems integration and interoperability. The solution enables the analysis, design, and implementation of dynamic, interoperable multi-domain systems without requesting continuous revision of existing specifications.

• Klíčová slova
• 5P medicine, architecture, ecosystem, health transformation, integration, interoperability, knowledge representation and management, modeling,
• Publikační typ
• časopisecké články MeSH

As the amount of genome information increases rapidly, there is a correspondingly greater need for methods that provide accurate and automated annotation of gene function. For example, many high-throughput technologies--e.g., next-generation sequencing--are being used today to generate lists of genes associated with specific conditions. However, their functional interpretation remains a challenge and many tools exist trying to characterize the function of gene-lists. Such systems rely typically in enrichment analysis and aim to give a quick insight into the underlying biology by presenting it in a form of a summary-report. While the load of annotation may be alleviated by such computational approaches, the main challenge in modern annotation remains to develop a systems form of analysis in which a pipeline can effectively analyze gene-lists quickly and identify aggregated annotations through computerized resources. In this article we survey some of the many such tools and methods that have been developed to automatically interpret the biological functions underlying gene-lists. We overview current functional annotation aspects from the perspective of their epistemology (i.e., the underlying theories used to organize information about gene function into a body of verified and documented knowledge) and find that most of the currently used functional annotation methods fall broadly into one of two categories: they are based either on 'known' formally-structured ontology annotations created by 'experts' (e.g., the GO terms used to describe the function of Entrez Gene entries), or--perhaps more adventurously--on annotations inferred from literature (e.g., many text-mining methods use computer-aided reasoning to acquire knowledge represented in natural languages). Overall however, deriving detailed and accurate insight from such gene lists remains a challenging task, and improved methods are called for. In particular, future methods need to (1) provide more holistic insight into the underlying molecular systems; (2) provide better follow-up experimental testing and treatment options, and (3) better manage gene lists derived from organisms that are not well-studied. We discuss some promising approaches that may help achieve these advances, especially the use of extended dictionaries of biomedical concepts and molecular mechanisms, as well as greater use of annotation benchmarks.

• Klíčová slova
• Benchmarks, Functional annotation, GO term enrichment, Keyword enhancement, Systems biology, Text mining,
• MeSH
• data mining metody   trendy   MeSH
• databáze genetické * trendy   MeSH
• genová ontologie * trendy   MeSH
• lidé MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

The aim of this article is to present a design of a Medical Knowledge Representation System (MEKRES). The system automatically offers relevant formalized knowledge by extended GLIF (Guidelines Interchange Format) models to participants (patient, physician, operator, ..) on the basis of acquired data. This selection algorithm is based on key attributes and cooperation with knowledge ontologies.

• MeSH
• algoritmy * MeSH
• expertní systémy MeSH
• lidé MeSH
• počítačová simulace MeSH
• směrnice pro lékařskou praxi jako téma MeSH
• systémy pro podporu klinického rozhodování organizace a řízení   MeSH
• umělá inteligence MeSH
• znalostní báze * MeSH
• zpracování přirozeného jazyka MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The paper describes procedures and a tool we have developed to simplify and speed up creating of Czech biomedical ontologies. Our method is based on searching for concepts in a corpus of medical texts and binding those concepts to an established international ontology. The new ontology will have two major advantages: it will be compatible with the international ontology and it will possibly cover all concepts used in the Czech healthcare. The tool supports an author of ontology by mechanizing some routine tasks that occurs in the process of an ontology creation. It tries to learn how to identify concepts in texts and how to bind them to the ontology. The tool then displays the suggestions to a user, who can correct them and add some new ones. Based on this feedback the tool adjusts rules for concept finding and binding. To accomplish such behaviour we have employed some natural language processing methods and information extraction tools.

• MeSH
• lékařská informatika organizace a řízení   MeSH
• rozvoj plánování * MeSH
• systémy pro podporu klinického rozhodování MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH

Electronic healthcare documentation is the key element of electronic healthcare (eHealth). Electronic oral health record (EOHR) supporting oral medicine is discussed. To provide dentists with a methodology and instrument to create oral health documentation in more efficient way, support information exchange and integration in dental domain and to ease dental decision-making and forensic dentistry identification tasks.The proposed methodology is used to model lifelong EOHR based on a small specific ontology where the use of other classification systems and nomenclatures, e.g. SNODENT, is possible. EOHRwith Lifelong DentCross user interface was developed and it has been supporting dental care at the University Hospital in Prague-Motol. The user interface is working in four languages and controlled by voice or keyboard. Lifelong DentCross user interface is reflecting the way of the work in dentistry and the EOHR can provide both structured and free text information to oral medicine.

• MeSH
• chorobopisy - počítačové systémy * MeSH
• elektronické zdravotní záznamy * MeSH
• informační technologie MeSH
• lékařská informatika * MeSH
• lidé MeSH
• stomatologie * MeSH
• uživatelské rozhraní počítače MeSH
• zubní lékaři MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Health and social care systems around the globe currently undergo a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental and behavioral context. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. For enabling communication and cooperation between actors from different domains using different methodologies, languages and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystems and all its components in structure, function and relationships in the necessary detail ranging from elementary particles up to the universe. That way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business view of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture.

• Klíčová slova
• Ecosystems, Health and social care transformation, Information modelling, Knowledge representation and management, Language types, Ontologies, Systems architecture,
• MeSH
• individualizovaná medicína * MeSH
• lidé MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Background/Objectives: Health and social care systems around the globe are currently undergoing a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental, and behavioral contexts. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. Methods: To enable communication and cooperation between actors from different domains using different methodologies, languages, and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystem and all its components in terms of structure, function and relationships in the necessary detail, ranging from elementary particles up to the universe. In this way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct, and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business viewpoint of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Results: The model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture. The formal representation of any ecosystem and its development process including examples of practical deployment of the approach, are presented in detail. This includes correct systems and standards integration and interoperability solutions. A special issue newly addressed in the paper is the correct and consistent formal representation Conclusions: of all components in the development process, enabling interoperability between and integration of any existing representational artifacts such as models, work products, as well as used terminologies and ontologies. The provided solution is meanwhile mandatory at ISOTC215, CEN/TC251 and many other standards developing organization in health informatics for all projects covering more than just one domain.

• Klíčová slova
• ecosystems, health and social care transformation, information modelling, knowledge representation and management, language types, ontologies, systems architecture,
• Publikační typ
• časopisecké články MeSH