Results for 'SNOMED'

16 found
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  1. SNOMED CT standard ontology based on the ontology for general medical science.Shaker El-Sappagh, Francesco Franda, Ali Farman & Kyung-Sup Kwak - 2018 - BMC Medical Informatics and Decision Making 76 (18):1-19.
    Background: Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is acomprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic healthdata. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but theseefforts have been hampered by the size and complexity of SCT. Method: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the termsin SCT in a way that (...)
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  2. Would SNOMED CT benefit from realism-based ontology evolution?Werner Ceusters, Kent Spackman & Barry Smith - 2007 - AMIA Annual Symposium Proceedings 2007:105-109.
    If SNOMED CT is to serve as a biomedical reference terminology, then steps must be taken to ensure comparability of information formulated using successive versions. New releases are therefore shipped with a history mechanism. We assessed the adequacy of this mechanism for its treatment of the distinction between changes occurring on the side of entities in reality and changes in our understanding thereof. We found that these two types are only partially distinguished and that a more detailed study is (...)
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  3. Ontology-based error detection in SNOMED-CT.Werner Ceusters, Barry Smith, Anand Kumar & Christoffel Dhaen - 2004 - Proceedings of Medinfo 2004:482-6.
    Quality assurance in large terminologies is a difficult issue. We present two algorithms that can help terminology developers and users to identify potential mistakes. We demon­strate the methodology by outlining the different types of mistakes that are found when the algorithms are applied to SNOMED-CT. On the basis of the results, we argue that both formal logical and linguistic tools should be used in the development and quality-assurance process of large terminologies.
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  4. Investigating Subsumption in SNOMED CT: An Exploration into Large Description Logic-Based Biomedical Terminologies.Olivier Bodenreider, Barry Smith, Anand Kumar & Anita Burgun - 2007 - Artificial Intelligence in Medicine 39 (3):183-195.
    Formalisms based on one or other flavor of Description Logic (DL) are sometimes put forward as helping to ensure that terminologies and controlled vocabularies comply with sound ontological principles. The objective of this paper is to study the degree to which one DL-based biomedical terminology (SNOMED CT) does indeed comply with such principles. We defined seven ontological principles (for example: each class must have at least one parent, each class must differ from its parent) and examined the properties of (...)
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  5. Information-theoretic classification of SNOMED improves the organization of context-sensitive excerpts from Cochrane Reviews.Sam Lee, Borlawsky Tara, Tao Ying, Li Jianrong, Friedman Carol, Barry Smith & A. Lussier Yves - 2007 - In Ron Rudnicki (ed.), Proceedings of the Annual Symposium of the American Medical Informatics Association. AMIA. pp. 645.
    The emphasis on evidence based medicine (EBM) has placed increased focus on finding timely answers to clinical questions in presence of patients. Using a combination of natural language processing for the generation of clinical excerpts and information theoretic distance based clustering, we evaluated multiple approaches for the efficient presentation of context-sensitive EBM excerpts.
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  6. Investigating subsumption in DL-based terminologies: A case study in SNOMED CT.Olivier Bodenreider, Barry Smith, Anand Kumar & Anita Burgun - 2004 - In Olivier Bodenreider, Barry Smith, Anand Kumar & Anita Burgun (eds.), Proceedings of the First International Workshop on Formal Biomedical Knowledge Representation (KR-MED 2004). pp. 12-20.
    Formalisms such as description logics (DL) are sometimes expected to help terminologies ensure compliance with sound ontological principles. The objective of this paper is to study the degree to which one DL-based biomedical terminology (SNOMED CT) complies with such principles. We defined seven ontological principles (for example: each class must have at least one parent, each class must differ from its parent) and examined the properties of SNOMED CT classes with respect to these principles. Our major results are: (...)
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  7. Mistakes in medical ontologies: Where do they come from and how can they be detected?Werner Ceusters, Barry Smith, Anand Kumar & Christoffel Dhaen - 2004 - Studies in Health and Technology Informatics 102:145-164.
    We present the details of a methodology for quality assurance in large medical terminologies and describe three algorithms that can help terminology developers and users to identify potential mistakes. The methodology is based in part on linguistic criteria and in part on logical and ontological principles governing sound classifications. We conclude by outlining the results of applying the methodology in the form of a taxonomy different types of errors and potential errors detected in SNOMED-CT.
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  8. Biomedical Ontologies.Barry Smith - 2022 - In Peter L. Elkin (ed.), Terminology, Ontology and Their Implementations: Teaching Guide and Notes. Springer. pp. 125-169.
    We begin at the beginning, with an outline of Aristotle’s views on ontology and with a discussion of the influence of these views on Linnaeus. We move from there to consider the data standardization initiatives launched in the 19th century, and then turn to investigate how the idea of computational ontologies developed in the AI and knowledge representation communities in the closing decades of the 20th century. We show how aspects of this idea, particularly those relating to the use of (...)
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  9. Biomedical ontology alignment: An approach based on representation learning.Prodromos Kolyvakis, Alexandros Kalousis, Barry Smith & Dimitris Kiritsis - 2018 - Journal of Biomedical Semantics 9 (21).
    While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic (...)
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  10. Biomedizinische Ontologien für die Praxis.M. Brochhausen & Barry Smith - 2009 - European Journal for Biomedical Informatics 1.
    Hintergrund: Biomedizinische Ontologien existieren unter anderem zur Integration von klinischen und experimentellen Daten. Um dies zu erreichen ist es erforderlich, dass die fraglichen Ontologien von einer großen Zahl von Benutzern zur Annotation von Daten verwendet werden. Wie können Ontologien das erforderliche Maß an Benutzerfreundlichkeit, Zuverlässigkeit, Kosteneffektivität und Domänenabdeckung erreichen, um weitreichende Akzeptanz herbeizuführen? -/- Material und Methoden: Wir konzentrieren uns auf zwei unterschiedliche Strategien, die zurzeit hierbei verfolgt werden. Eine davon wird von SNOMED CT im Bereich der Medizin vertreten, (...)
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  11. The significance of SNODENT.Louis Goldberg, Werner Ceusters, John Eisner & Barry Smith - 2005 - Medical Informatics Europe 2005: 737-742.
    SNODENT is a dental diagnostic vocabulary incompletely integrated in SNOMED-CT. Nevertheless, SNODENT could become the de facto standard for dental diagnostic coding. SNODENT's manageable size, the fact that it is administratively self-contained, and relates to a well-understood domain provides valuable opportunities to formulate and test, in controlled experiments, a series of hypothesis concerning diagnostic systems. Of particular interest are questions related to establishing appropriate quality assurance methods for its optimal level of detail in content, its ontological structure, its construction (...)
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  12. Putting Biomedical Ontologies to Work.Barry Smith & Mathias Brochhausen - 2010 - Methods of Information in Medicine 49 (2):135-40.
    Biomedical ontologies exist to serve integration of clinical and experimental data, and it is critical to their success that they be put to widespread use in the annotation of data. How, then, can ontologies achieve the sort of user-friendliness, reliability, cost-effectiveness, and breadth of coverage that is necessary to ensure extensive usage? Methods: Our focus here is on two different sets of answers to these questions that have been proposed, on the one hand in medicine, by the SNOMED CT (...)
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  13. Using philosophy to improve the coherence and interoperability of applications ontologies: A field report on the collaboration of IFOMIS and L&C.Jonathan Simon, James Matthew Fielding & Barry Smith - 2004 - In Gregor Büchel, Bertin Klein & Thomas Roth-Berghofer (eds.), Proceedings of the First Workshop on Philosophy and Informatics. Deutsches Forschungs­zentrum für künstliche Intelligenz, Cologne: 2004 (CEUR Workshop Proceedings 112). pp. 65-72.
    The collaboration of Language and Computing nv (L&C) and the Institute for Formal Ontology and Medical Information Science (IFOMIS) is guided by the hypothesis that quality constraints on ontologies for software ap-plication purposes closely parallel the constraints salient to the design of sound philosophical theories. The extent of this parallel has been poorly appreciated in the informatics community, and it turns out that importing the benefits of phi-losophical insight and methodology into application domains yields a variety of improvements. L&C’s LinKBase® (...)
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  14. Biomedical informatics and granularity.Anand Kumar & Barry Smith - 2004 - Comparative and Functional Genomics 5 (6-7):501-508.
    An explicit formal-ontological representation of entities existing at multiple levels of granularity is an urgent requirement for biomedical information processing. We discuss some fundamental principles which can form a basis for such a representation. We also comment on some of the implicit treatments of granularity in currently available ontologies and terminologies (GO, FMA, SNOMED CT).
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  15. Negative findings in electronic health records and biomedical ontologies: a realist approach.Werner Ceusters, Peter Elkin & Barry Smith - 2007 - International Journal of Medical Informatics 76 (3):S326-S333.
    PURPOSE—A substantial fraction of the observations made by clinicians and entered into patient records are expressed by means of negation or by using terms which contain negative qualifiers (as in “absence of pulse” or “surgical procedure not performed”). This seems at first sight to present problems for ontologies, terminologies and data repositories that adhere to a realist view and thus reject any reference to putative non-existing entities. Basic Formal Ontology (BFO) and Referent Tracking (RT) are examples of such paradigms. The (...)
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  16. Six questions on the construction of ontologies in biomedicine.Anand Kumar, A. Burgun, W. Ceusters, J. Cimino, J. Davis, P. Elkin, I. Kalet, A. Rector, J. Rice, J. Rogers, Barry Smith & Others - 2005 - Report of the AMIA Working Group on Formal Biomedical Knowledge Representation 1.
    (Report assembled for the Workshop of the AMIA Working Group on Formal Biomedical Knowledge Representation in connection with AMIA Symposium, Washington DC, 2005.) Best practices in ontology building for biomedicine have been frequently discussed in recent years. However there is a range of seemingly disparate views represented by experts in the field. These views not only reflect the different uses to which ontologies are put, but also the experiences and disciplinary background of these experts themselves. We asked six questions related (...)
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