90,522 research outputs found
The Requirements for Ontologies in Medical Data Integration: A Case Study
Evidence-based medicine is critically dependent on three sources of
information: a medical knowledge base, the patients medical record and
knowledge of available resources, including where appropriate, clinical
protocols. Patient data is often scattered in a variety of databases and may,
in a distributed model, be held across several disparate repositories.
Consequently addressing the needs of an evidence-based medicine community
presents issues of biomedical data integration, clinical interpretation and
knowledge management. This paper outlines how the Health-e-Child project has
approached the challenge of requirements specification for (bio-) medical data
integration, from the level of cellular data, through disease to that of
patient and population. The approach is illuminated through the requirements
elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three
diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database
Engineering & Applications Symposium (Ideas2007). Banff, Canada September
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An ontology to standardize research output of nutritional epidemiology : from paper-based standards to linked content
Background: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology.
Methods: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts.
Results: Ontologies for food and nutrition (n = 37), disease and specific population (n = 100), data description (n = 21), research description (n = 35), and supplementary (meta) data description (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts.
Conclusion: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology
Developing Ontology Support for Human Malaria Control Initiatives
Malaria is one of the most common infectious diseases and an enormous public health problem in Sub-Sahara Africa, Asia and parts of America. In this paper, we discuss the development of the Human Malaria Control Ontology (HMCO) which contains general information on Malaria and epidemiological information that can help in the formulation of effective malaria control policies. The HMCO is aimed at providing interoperability support for the knowledge management of malaria control initiatives, and serve as an open semantic web infrastructure for malaria research and treatment
Developing Ontology Support for Human Malaria Control Initiatives
Malaria is one of the most common infectious
diseases and an enormous public health problem in
Sub-Sahara Africa, Asia and parts of America. In this
paper, we discuss the development of the Human
Malaria Control Ontology (HMCO) which contains
general information on Malaria and epidemiological
information that can help in the formulation of
effective malaria control policies. The HMCO is
aimed at providing interoperability support for the
knowledge management of malaria control
initiatives, and serve as an open semantic web
infrastructure for malaria research and treatment
Ontology modelling methodology for temporal and interdependent applications
The increasing adoption of Semantic Web technology by several classes of applications in recent years, has made ontology engineering a crucial part of application development. Nowadays, the abundant accessibility of interdependent information from multiple resources and representing various fields such as health, transport, and banking etc., further evidence the growing need for utilising ontology for the development of Web applications. While there have been several advances in the adoption of the ontology for application development, less emphasis is being made on the modelling methodologies for representing modern-day application that are characterised by the temporal nature of the data they process, which is captured from multiple sources. Taking into account the benefits of a methodology in the system development, we propose a novel methodology for modelling ontologies representing Context-Aware Temporal and Interdependent Systems (CATIS). CATIS is an ontology development methodology for modelling temporal interdependent applications in order to achieve the desired results when modelling sophisticated applications with temporal and inter dependent attributes to suit today's application requirements
Ontology-based knowledge representation of experiment metadata in biological data mining
According to the PubMed resource from the U.S. National Library of Medicine,
over 750,000 scientific articles have been published in the ~5000 biomedical journals
worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes
empathi: An ontology for Emergency Managing and Planning about Hazard Crisis
In the domain of emergency management during hazard crises, having sufficient
situational awareness information is critical. It requires capturing and
integrating information from sources such as satellite images, local sensors
and social media content generated by local people. A bold obstacle to
capturing, representing and integrating such heterogeneous and diverse
information is lack of a proper ontology which properly conceptualizes this
domain, aggregates and unifies datasets. Thus, in this paper, we introduce
empathi ontology which conceptualizes the core concepts concerning with the
domain of emergency managing and planning of hazard crises. Although empathi
has a coarse-grained view, it considers the necessary concepts and relations
being essential in this domain. This ontology is available at
https://w3id.org/empathi/
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