Empirical analysis of impacts of instance-driven changes in ontologies

Changes in the characterization of instances in digital contents are one of the rationales to change or evolve ontologies which support the domain. These changes can impacts on one or more of interrelated ontologies. Before implementing changes, their impact on the target ontology, other dependent ontologies or dependent systems should be analysed. We investigate three concerns for the determination of impacts of changes in ontologies: representation of changes to ensure minimum impact, impact determination and integrity determination. Key elements of our solution are the operationalization of change operations to minimize impacts, a parameterization approach for the determination of impacts, a categorization scheme for identified impacts, and prioritization technique for change operations based on the severity of impacts

Ontology and medical terminology: Why description logics are not enough

Ontology is currently perceived as the solution of first resort for all problems related to biomedical terminology, and the use of description logics is seen as a minimal requirement on adequate ontology-based systems. Contrary to common conceptions, however, description logics alone are not able to prevent incorrect representations; this is because they do not come with a theory indicating what is computed by using them, just as classical arithmetic does not tell us anything about the entities that are added or subtracted. In this paper we shall show that ontology is indeed an essential part of any solution to the problems of medical terminology – but only if it is understood in the right sort of way. Ontological engineering, we shall argue, should in every case go hand in hand with a sound ontological theory

Clinical Terminology in Patient Health Record System - SNOMED CT Overview

Background of study: Patient Health Record System (PHRS) is used byphysicians for capturing patient medical records in electronic media.Standardization in PHRS arises a major challenge due to its complexities. Theused of clinical terminology is needed in order to facilitate more expressiveclinical data input, provide unambiguous encoding and support the exchange ofclinical information. One of highly specialized clinical terminology is SNOMEDCT(Systematized Nomenclature of Medicine Clinical Terms) that able to encodeclinical data, and contains concepts that linked to clinical knowledge to enableaccurate recording of data without ambiguity. The aims of this paper is to discussthe use of clinical terminology in PHRS and identifying importance factors forapplying clinical terminology in healthcare services.Method: This study used review of literature in order to find the use of clinicalterminology in patient health record system by reviewing current used of clinicalterminology.Result: The result of the study found that clinical terminology supportsinformation exchange between healthcare providers

Clinical Terminology in Patient Health Record System - SNOMED CT Overview

Background of study: Patient Health Record System (PHRS) is used by physicians for capturing patient medical records in electronic media. Standardization in PHRS arises a major challenge due to its complexities. The used of clinical terminology is needed in order to facilitate more expressive clinical data input, provide unambiguous encoding and support the exchange of clinical information. One of highly specialized clinical terminology is SNOMED CT(Systematized Nomenclature of Medicine Clinical Terms) that able to encode clinical data, and contains concepts that linked to clinical knowledge to enable accurate recording of data without ambiguity. The aims of this paper is to discuss the use of clinical terminology in PHRS and identifying importance factors for applying clinical terminology in healthcare services. Method: This study used review of literature in order to find the use of clinical terminology in patient health record system by reviewing current used of clinical terminology. Result: The result of the study found that clinical terminology supports information exchange between healthcare provider

Evolution in the Ontology Based Knowledge Management Systems

An ontology-based knowledge management system uses an ontology to represent explicit specification of a business domain and to serve as a backbone for providing and searching for knowledge sources. But, dynamically changing business environment implies changes in the conceptualisation of a business domain that are reflected on the underlying domain ontologies. Consequently, these changes have effects on the performance and validity of the KM system. In this paper we make an analysis of the problems induced by using not-evolved ontologies and present an approach for enabling consistency of the description of knowledge sources in an ontology-based KM system in the case of changes in the domain ontology. This approach is based on our research on ontology evolution and ontology-based annotation of documents. The proposed method is implemented in our semantic annotation framework so that efficient acquiring and maintaining of ontology-based metadata is supported

Managing Requirement Volatility in an Ontology-Driven Clinical LIMS Using Category Theory. International Journal of Telemedicine and Applications

Requirement volatility is an issue in software engineering in general, and in Web-based clinical applications in particular, which often originates from an incomplete knowledge of the domain of interest. With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain. At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility. In this paper, we present a novel agent-based approach for analyzing and managing volatile and dynamic requirements in an ontology-driven laboratory information management system (LIMS) designed for Web-based case reporting in medical mycology. The proposed framework is empowered with ontologies and formalized using category theory to provide a deep and common understanding of the functional and nonfunctional requirement hierarchies and their interrelations, and to trace the effects of a change on the conceptual framework.Comment: 36 Pages, 16 Figure

Requirements for Implementing Mappings Adaptation Systems

International audienceOntologies, or more generally speaking, Knowledge Organization Systems (KOS) have been developed to support the correct interpretation of shared data in collaborative applications. The quantity and the heterogeneity of domain knowledge often require several KOS to describe their content. In order to assure unambiguous interpretation, overlapped concepts of different, but domain-related KOS are semantically connected via mappings. However, in various domains, KOS periodically evolve creating the necessity of reviewing the validity of associated mappings. The size of KOS remains a barrier for a manual review of mappings, and rather requires the support of (semi-) automatic solutions. This article describes our experiences in understanding how KOS evolution affects mappings. We present our lessons learned from various empirical experiments, and we derive primary elements and requirements for improving the automation of mapping maintenance

Developing techniques for enhancing comprehensibility of controlled medical terminologies

A controlled medical terminology (CMT) is a collection of concepts (or terms) that are used in the medical domain. Typically, a CMT also contains attributes of those concepts and/or relationships between those concepts. Electronic CMTs are extremely useful and important for communication between and integration of independent information systems in healthcare, because data in this area is highly fragmented. A single query in this area might involve several databases, e.g., a clinical database, a pharmacy database, a radiology database, and a lab test database. Unfortunately, the extensive sizes of CMTs, often containing tens of thousands of concepts and hundreds of thousands of relationships between pairs of those concepts, impose steep learning curves for new users of such CMTs. In this dissertation, we address the problem of helping a user to orient himself in an existing large CMT. In order to help a user comprehend a large, complex CMT, we need to provide abstract views of the CMT. However, at this time, no tools exist for providing a user with such abstract views. One reason for the lack of tools is the absence of a good theory on how to partition an overwhelming CMT into manageable pieces. In this dissertation, we try to overcome the described problem by using a threepronged approach. (1) We use the power of Object-Oriented Databases to design a schema extraction process for large, complex CMTs. The schema resulting from this process provides an excellent, compact representation of the CMT. (2) We develop a theory and a methodology for partitioning a large OODI3 schema, modeled as a graph, into small meaningful units. The methodology relies on the interaction between a human and a computer, making optimal use of the human\u27s semantic knowledge and the computer\u27s speed. Furthermore, the theory and methodology developed for the scbemalevel partitioning are also adapted to the object-level of a CMT. (3) We use purely structural similarities for partitioning CMTs, eliminating the need for a human expert in the partitioning methodology mentioned above. Two large medical terminologies are used as our test beds, the Medical Entities Dictionary (MED) and the Unified Medical Language System (UMLS), which itself contains a number of terminologies