Results for 'biomedical informatics'

484 found
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  1. 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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  2. Discussion of “Biomedical informatics: We are what we publish”.Geissbuhler Antoine, W. E. Hammond, A. Hasman, R. Hussein, R. Koppel, C. A. Kulikowski, V. Maojo, F. Martin-Sanchez, P. W. Moorman, Moura La, F. G. De Quiros, M. J. Schuemle, Barry Smith & J. Talmon - 2013 - Methods of Information in Medicine 52 (6):547-562.
    This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Biomedical Informatics: We Are What We Publish", written by Peter L. Elkin, Steven H. Brown, and Graham Wright. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Elkin et al. paper. In subsequent issues the discussion can continue through letters to the editor.
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  3. Ontology as the core discipline of biomedical informatics: Legacies of the past and recommendations for the future direction of research.Barry Smith & Werner Ceusters - 2007 - In Gordana Dodig Crnkovic & Susan Stuart (eds.), Computation, Information, Cognition: The Nexus and the Liminal. Cambridge Scholars Publishing. pp. 104-122.
    The automatic integration of rapidly expanding information resources in the life sciences is one of the most challenging goals facing biomedical research today. Controlled vocabularies, terminologies, and coding systems play an important role in realizing this goal, by making it possible to draw together information from heterogeneous sources – for example pertaining to genes and proteins, drugs and diseases – secure in the knowledge that the same terms will also represent the same entities on all occasions of use. In (...)
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  4. 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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  5. Biomedical Ontologies.Barry Smith - forthcoming - In Peter Elkin (ed.), Terminology, Ontology and their Implementations. Springer Nature Switzerland AG.
    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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  6. A Unified Framework for Biomedical Terminologies and Ontologies.Werner Ceusters & Barry Smith - 2010 - Studies in Health Technology and Informatics 160:1050-1054.
    The goal of the OBO (Open Biomedical Ontologies) Foundry initiative is to create and maintain an evolving collection of non-overlapping interoperable ontologies that will offer unambiguous representations of the types of entities in biological and biomedical reality. These ontologies are designed to serve non-redundant annotation of data and scientific text. To achieve these ends, the Foundry imposes strict requirements upon the ontologies eligible for inclusion. While these requirements are not met by most existing biomedical terminologies, the latter (...)
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  7. National Center for Biomedical Ontology: Advancing biomedicine through structured organization of scientific knowledge.Daniel L. Rubin, Suzanna E. Lewis, Chris J. Mungall, Misra Sima, Westerfield Monte, Ashburner Michael, Christopher G. Chute, Ida Sim, Harold Solbrig, M. A. Storey, Barry Smith, John D. Richter, Natasha Noy & Mark A. Musen - 2006 - Omics: A Journal of Integrative Biology 10 (2):185-198.
    The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) (...)
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  8. Biomedical imaging ontologies: A survey and proposal for future work.Barry Smith, Sivaram Arabandi, Mathias Brochhausen, Michael Calhoun, Paolo Ciccarese, Scott Doyle, Bernard Gibaud, Ilya Goldberg, Charles E. Kahn Jr, James Overton, John Tomaszewski & Metin Gurcan - 2015 - Journal of Pathology Informatics 6 (37):37.
    Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as “cell” or “image” or “tissue” or “microscope”) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical defi nitions thereby also supporting reasoning over the tagged data. Aim: This (...)
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  9. The National Center for Biomedical Ontology: Advancing Biomedicine through Structured Organization of Scientific Knowledge. Rubin - 2012 - Journal of the American Medical Informatics Association 19 (2):190-195.
    The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups who develop (...)
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  10. The Role of Foundational Relations in the Alignment of Biomedical Ontologies.Barry Smith & Cornelius Rosse - 2004 - In M. Fieschi, E. Coiera & Y.-C. J. Li (eds.), Medinfo. IOS Press. pp. 444-448.
    The Foundational Model of Anatomy (FMA) symbolically represents the structural organization of the human body from the macromolecular to the macroscopic levels, with the goal of providing a robust and consistent scheme for classifying anatomical entities that is designed to serve as a reference ontology in biomedical informatics. Here we articulate the need for formally clarifying the is-a and part-of relations in the FMA and similar ontology and terminology systems. We diagnose certain characteristic errors in the treatment of (...)
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  11. Towards Industrial Strength Philosophy: How Analytical Ontology Can Help Medical Informatics.Barry Smith & Werner Ceusters - 2003 - Interdisciplinary Science Reviews 28 (2):106–111.
    Initially the problems of data integration, for example in the field of medicine, were resolved in case by case fashion. Pairs of databases were cross-calibrated by hand, rather as if one were translating from French into Hebrew. As the numbers and complexity of database systems increased, the idea arose of streamlining these efforts by constructing one single benchmark taxonomy, as it were a central switchboard, into which all of the various classification systems would need to be translated only once. By (...)
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  12. The National Center for Biomedical Ontology.Mark A. Musen, Natalya F. Noy, Nigam H. Shah, Patricia L. Whetzel, Christopher G. Chute, Margaret-Anne Story & Barry Smith - 2012 - Journal of the American Medical Informatics Association 19 (2):190-195.
    The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups who develop (...)
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  13. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain.Barry Smith, Waclaw Kusnierczyk, Daniel Schober, & Werner Ceusters - 2006 - In Proceedings of KR-MED, CEUR, vol. 222. pp. 57-65.
    Ontology is a burgeoning field, involving researchers from the computer science, philosophy, data and software engineering, logic, linguistics, and terminology domains. Many ontology-related terms with precise meanings in one of these domains have different meanings in others. Our purpose here is to initiate a path towards disambiguation of such terms. We draw primarily on the literature of biomedical informatics, not least because the problems caused by unclear or ambiguous use of terms have been there most thoroughly addressed. We (...)
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  14. Strengths and Limitations of Formal Ontologies in the Biomedical Domain.Barry Smith - 2009 - Electronic Journal of Communication, Information and Innovation in Health 3 (1):31-45.
    We propose a typology of representational artifacts for health care and life sciences domains and associate this typology with different kinds of formal ontology and logic, drawing conclusions as to the strengths and limitations for ontology in a description logics framework. The four types of domain representation we consider are: (i) lexico-semantic representation, (ii) representation of types of entities, (iii) representations of background knowledge, and (iv) representation of individuals. We advocate a clear distinction of the four kinds of representation in (...)
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  15.  94
    Philosophy and Biomedical Information Systems.Barry Smith & Bert Klagges - 2008 - In Katherine Munn & Barry Smith (eds.), Applied Ontology: An Introduction. Ontos. pp. 17-30.
    The pathbreaking scientific advances of recent years call for a new philosophical consideration of the fundamental categories of biology and its neighboring disciplines. Above all, the new information technologies used in biomedical research, and the necessity to master the continuously growing flood of data that is associated therewith, demand a profound and systematic reflection on the systematization and classification of biological data. This, however, demands robust theories of basic concepts such as kind, species, part, whole, function, process, fragment, sequence, (...)
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  16. A strategy for improving and integrating biomedical ontologies.Cornelius Rosse, Anand Kumar, Jose L. V. Mejino, Daniel L. Cook, Landon T. Detwiler & Barry Smith - 2005 - In Proceedings of the Annual Symposium of the American Medical Informatics Association. AMIA. pp. 639-643.
    The integration of biomedical terminologies is indispensable to the process of information integration. When terminologies are linked merely through the alignment of their leaf terms, however, differences in context and ontological structure are ignored. Making use of the SNAP and SPAN ontologies, we show how three reference domain ontologies can be integrated at a higher level, through what we shall call the OBR framework (for: Ontology of Biomedical Reality). OBR is designed to facilitate inference across the boundaries of (...)
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  17. From concepts to clinical reality: An essay on the benchmarking of biomedical terminologies.Barry Smith - 2006 - Journal of Biomedical Informatics 39 (3):288-298.
    It is only by fixing on agreed meanings of terms in biomedical terminologies that we will be in a position to achieve that accumulation and integration of knowledge that is indispensable to progress at the frontiers of biomedicine. Standardly, the goal of fixing meanings is seen as being realized through the alignment of terms on what are called ‘concepts’. Part I addresses three versions of the concept-based approach – by Cimino, by Wüster, and by Campbell and associates – and (...)
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  18. Biodynamic Ontology: Applying BFO in the Biomedical Domain.Barry Smith, Pierre Grenon & Louis Goldberg - 2004 - Studies in Health and Technology Informatics 102:20–38.
    Current approaches to formal representation in biomedicine are characterized by their focus on either the static or the dynamic aspects of biological reality. We here outline a theory that combines both perspectives and at the same time tackles the by no means trivial issue of their coherent integration. Our position is that a good ontology must be capable of accounting for reality both synchronically (as it exists at a time) and diachronically (as it unfolds through time), but that these are (...)
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  19. 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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  20. Formal Ontology for Natural Language Processing and the Integration of Biomedical Databases.Jonathan Simon, James M. Fielding, Mariana C. Dos Santos & Barry Smith - 2005 - International Journal of Medical Informatics 75 (3-4):224-231.
    The central hypothesis of the collaboration between Language and Computing (L&C) and the Institute for Formal Ontology and Medical Information Science (IFOMIS) is that the methodology and conceptual rigor of a philosophically inspired formal ontology greatly benefits application ontologies. To this end r®, L&C’s ontology, which is designed to integrate and reason across various external databases simultaneously, has been submitted to the conceptual demands of IFOMIS’s Basic Formal Ontology (BFO). With this project we aim to move beyond the level of (...)
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  21. The Neurological Disease Ontology.Mark Jensen, Alexander P. Cox, Naveed Chaudhry, Marcus Ng, Donat Sule, William Duncan, Patrick Ray, Bianca Weinstock-Guttman, Barry Smith, Alan Ruttenberg, Kinga Szigeti & Alexander D. Diehl - 2013 - Journal of Biomedical Semantics 4 (42):42.
    We are developing the Neurological Disease Ontology (ND) to provide a framework to enable representation of aspects of neurological diseases that are relevant to their treatment and study. ND is a representational tool that addresses the need for unambiguous annotation, storage, and retrieval of data associated with the treatment and study of neurological diseases. ND is being developed in compliance with the Open Biomedical Ontology Foundry principles and builds upon the paradigm established by the Ontology for General Medical Science (...)
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  22. Building Ontologies with Basic Formal Ontology.Robert Arp, Barry Smith & Andrew D. Spear - 2015 - Cambridge, MA: MIT Press.
    In the era of “big data,” science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that (...)
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  23. Controlled vocabularies in bioinformatics: A case study in the Gene Ontology.Barry Smith & Anand Kumar - 2004 - Drug Discovery Today: Biosilico 2 (6):246-252.
    The automatic integration of information resources in the life sciences is one of the most challenging goals facing biomedical informatics today. Controlled vocabularies have played an important role in realizing this goal, by making it possible to draw together information from heterogeneous sources secure in the knowledge that the same terms will also represent the same entities on all occasions of use. One of the most impressive achievements in this regard is the Gene Ontology (GO), which is rapidly (...)
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  24. The Ontology-Epistemology Divide: A Case Study in Medical Terminology.OIivier Bodenreider, Barry Smith & Anita Burgun - 2004 - In Achille Varzi & Laure Vieu (eds.), Formal Ontology in Information Systems. Proceedings of the Third International Conference (FOIS 2004). IOS Press.
    Medical terminology collects and organizes the many different kinds of terms employed in the biomedical domain both by practitioners and also in the course of biomedical research. In addition to serving as labels for biomedical classes, these names reflect the organizational principles of biomedical vocabularies and ontologies. Some names represent invariant features (classes, universals) of biomedical reality (i.e., they are a matter for ontology). Other names, however, convey also how this reality is perceived, measured, and (...)
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  25. Establishing and Harmonizing Ontologies in an Interdisciplinary Health Care and Clinical Research Environment.Barry Smith & Mathias Brochhausen - 2008 - Studies in Health, Technology and Informatics 134:219-234.
    Ontologies are being ever more commonly used in biomedical informatics and we provide a survey of some of these uses, and of the relations between ontologies and other terminology resources. In order for ontologies to become truly useful, two objectives must be met. First, ways must be found for the transparent evaluation of ontologies. Second, existing ontologies need to be harmonised. We argue that one key foundation for both ontology evaluation and harmonisation is the adoption of a realist (...)
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  26. Oncology ontology in the NCI Thesaurus.Anand Kumar & Barry Smith - 2005 - Artificial Intelligence in Medicine:213-220.
    The National Cancer Institute’s Thesaurus (NCIT) has been created with the goal of providing a controlled vocabulary which can be used by specialists in the various sub-domains of oncology. It is intended to be used for purposes of annotation in ways designed to ensure the integration of data and information deriving from these various sub-domains, and thus to support more powerful cross-domain inferences. In order to evaluate its suitability for this purpose, we examined the NCIT’s treatment of the kinds of (...)
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  27. VO: Vaccine Ontology.Yongqun He, Lindsay Cowell, Alexander D. Diehl, H. L. Mobley, Bjoern Peters, Alan Ruttenberg, Richard H. Scheuermann, Ryan R. Brinkman, Melanie Courtot, Chris Mungall, Barry Smith & Others - 2009 - In ICBO 2009: Proceedings of the First International Conference on Biomedical Ontology. Buffalo:
    Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine (...)
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  28. 基于基本形式化本体的本体构建.Robert Arp, Barry Smith & Andrew D. Spear - 2020 - Beijing: People's Medical Publishing House.
    In the era of “big data,” science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that (...)
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  29. Applied Ontology: An Introduction.Katherine Munn & Barry Smith (eds.) - 2008 - Frankfurt: ontos.
    Ontology is the philosophical discipline which aims to understand how things in the world are divided into categories and how these categories are related together. This is exactly what information scientists aim for in creating structured, automated representations, called 'ontologies,' for managing information in fields such as science, government, industry, and healthcare. Currently, these systems are designed in a variety of different ways, so they cannot share data with one another. They are often idiosyncratically structured, accessible only to those who (...)
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  30. 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 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 (...)
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  31. Revising the UMLS Semantic Network.Steffen Schulze-Kremer, Barry Smith & Anand Kumar - 2004 - In MedInfo.
    The integration of standardized biomedical terminologies into a single, unified knowledge representation system has formed a key area of applied informatics research in recent years. The Unified Medical Language System (UMLS) is the most advanced and most prominent effort in this direction, bringing together within its Metathesaurus a large number of distinct source-terminologies. The UMLS Semantic Network, which is designed to support the integration of these source-terminologies, has proved to be a highly successful combination of formal coherence and (...)
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  32. The representation of protein complexes in the Protein Ontology.Carol Bult, Harold Drabkin, Alexei Evsikov, Darren Natale, Cecilia Arighi, Natalia Roberts, Alan Ruttenberg, Peter D’Eustachio, Barry Smith, Judith Blake & Cathy Wu - 2011 - BMC Bioinformatics 12 (371):1-11.
    Representing species-specific proteins and protein complexes in ontologies that are both human and machine-readable facilitates the retrieval, analysis, and interpretation of genome-scale data sets. Although existing protin-centric informatics resources provide the biomedical research community with well-curated compendia of protein sequence and structure, these resources lack formal ontological representations of the relationships among the proteins themselves. The Protein Ontology (PRO) Consortium is filling this informatics resource gap by developing ontological representations and relationships among proteins and their variants and (...)
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  33. Development of a Manufacturing Ontology for Functionally Graded Materials.Francesco Furini, Rahul Rai, Barry Smith, Georgio Colombo & Venkat Krovi - 2016 - In Proceedings of International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE).
    The development of manufacturing technologies for new materials involves the generation of a large and continually evolving volume of information. The analysis, integration and management of such large volumes of data, typically stored in multiple independently developed databases, creates significant challenges for practitioners. There is a critical need especially for open-sharing of data pertaining to engineering design which together with effective decision support tools can enable innovation. We believe that ontology applied to engineering (OE) represents a viable strategy for the (...)
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  34. 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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  35. Genome Informatics: The Role of DNA in Cellular Computations.James A. Shapiro - 2006 - Biological Theory 1 (3):288-301.
    Cells are cognitive entities possessing great computational power. DNA serves as a multivalent information storage medium for these computations at various time scales. Information is stored in sequences, epigenetic modifications, and rapidly changing nucleoprotein complexes. Because DNA must operate through complexes formed with other molecules in the cell, genome functions are inherently interactive and involve two-way communication with various cellular compartments. Both coding sequences and repetitive sequences contribute to the hierarchical systemic organization of the genome. By virtue of nucleoprotein complexes, (...)
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  36.  79
    Informatics: Science or Téchne?Tito Palmeiro - 2016 - O Que Nos Faz Pensar 25:88-97.
    Informatics is generally understood as a “new technology” and is therewith discussed according to technological aspects such as speed, data retrieval, information control and so on. Its widespread use from home appliances to enterprises and universities is not the result of a clear-cut analysis of its inner possibilities but is rather dependent on all sorts of ideological promises of unlimited progress. We will discuss the theoretical definition of informatics proposed in 1936 by Alan Turing in order to show (...)
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  37. Informatics: the fuel for pharmacometric analysis.H. Grasela Thaddeus, Fiedler-Kelly Jill, Cirincione Brenda, Hitchcock Darcy, Reitz Kathleen, Sardella Susanne & Barry Smith - 2007 - AAPS Journal 9 (1):E84--E91.
    The current informal practice of pharmacometrics as a combination art and science makes it hard to appreciate the role that informatics can and should play in the future of the discipline and to comprehend the gaps that exist because of its absence. The development of pharmacometric informatics has important implications for expediting decision making and for improving the reliability of decisions made in model-based development. We argue that well-defined informatics for pharmacometrics can lead to much needed improvements (...)
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  38. 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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  39. Context-based task ontologies for clinical guidelines.Anand Kumar, Paolo Ciccarese, Barry Smith & Matteo Piazza - 2004 - In D. Pisanelli (ed.), Ontologies in Medicine: Proceedings of the Workshop on Medical Ontologies, Rome October 2003 (Studies in Health and Technology Informatics, 102). Amsterdam: IOS Press. pp. 81-94.
    Evidence-based medicine relies on the execution of clinical practice guidelines and protocols. A great deal of of effort has been invested in the development of various tools which automate the representation and execution of the recommendations contained within such guidelines and protocols by creating Computer Interpretable Guideline Models (CIGMs). Context-based task ontologies (CTOs), based on standard terminology systems like UMLS, form one of the core components of such a model. We have created DAML+OIL-based CTOs for the tasks mentioned in the (...)
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  40. 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, die (...)
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  41. Infectious Disease Ontology.Lindsay Grey Cowell & Barry Smith - 2009 - In Infectious Disease Informatics. New York: Springer New York. pp. 373-395.
    Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data (...)
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  42. Enhancement, Biomedical.Thomas Douglas - 2013 - In Hugh LaFollette (ed.), The International Encyclopedia of Ethics. Wiley-Blackwell.
    Biomedical technologies can increasingly be used not only to combat disease, but also to augment the capacities or traits of normal, healthy people – a practice commonly referred to as biomedical enhancement. Perhaps the best‐established examples of biomedical enhancement are cosmetic surgery and doping in sports. But most recent scientific attention and ethical debate focuses on extending lifespan, lifting mood, and augmenting cognitive capacities.
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  43.  94
    Biomedical Terminologies and Ontologies: Enabling Biomedical Semantic Interoperability and Standards in Europe.Bernard de Bono, Mathias Brochhausen, Sybo Dijkstra, Dipak Kalra, Stephan Keifer & Barry Smith - 2009 - In European Large-Scale Action on Electronic Health.
    In the management of biomedical data, vocabularies such as ontologies and terminologies (O/Ts) are used for (i) domain knowledge representation and (ii) interoperability. The knowledge representation role supports the automated reasoning on, and analysis of, data annotated with O/Ts. At an interoperability level, the use of a communal vocabulary standard for a particular domain is essential for large data repositories and information management systems to communicate consistently with one other. Consequently, the interoperability benefit of selecting a particular O/T as (...)
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  44. The Ontology for Biomedical Investigations.Anita Bandrowski, Ryan Brinkman, Mathias Brochhausen, Matthew H. Brush, Bill Bug, Marcus C. Chibucos, Kevin Clancy, Mélanie Courtot, Dirk Derom, Michel Dumontier, Liju Fan, Jennifer Fostel, Gilberto Fragoso, Frank Gibson, Alejandra Gonzalez-Beltran, Melissa A. Haendel, Yongqun He, Mervi Heiskanen, Tina Hernandez-Boussard, Mark Jensen, Yu Lin, Allyson L. Lister, Phillip Lord, James Malone, Elisabetta Manduchi, Monnie McGee, Norman Morrison, James A. Overton, Helen Parkinson, Bjoern Peters, Philippe Rocca-Serra, Alan Ruttenberg, Susanna-Assunta Sansone, Richard H. Scheuermann, Daniel Schober, Barry Smith, Larisa N. Soldatova, Christian J. Stoeckert, Chris F. Taylor, Carlo Torniai, Jessica A. Turner, Randi Vita, Patricia L. Whetzel & Jie Zheng - 2016 - PLoS ONE 11 (4):e0154556.
    The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding (...)
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  45. 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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  46. Semantics and Metaphysics in Informatics: Toward an Ontology of Tasks (a Reply to Lenartowicz et al. 2010, Towards an Ontology of Cognitive Control).Carrie Figdor - 2011 - Topics in Cognitive Science 3 (2):222-226.
    This article clarifies three principles that should guide the development of any cognitive ontology. First, that an adequate cognitive ontology depends essentially on an adequate task ontology; second, that the goal of developing a cognitive ontology is independent of the goal of finding neural implementations of the processes referred to in the ontology; and third, that cognitive ontologies are neutral regarding the metaphysical relationship between cognitive and neural processes.
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  47. 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 will (...)
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  48.  36
    Biomedical technocracy, the networked public sphere and the biopolitics of COVID-19: notes on the Agamben affair.Tim Christiaens - 2022 - Culture Theory and Critique 1 (63):1-18.
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  49. New desiderata for biomedical terminologies.Barry Smith - 2008 - In Katherine Munn & Barry Smith (eds.), Applied Ontology: An Introduction. Ontos. pp. 83-109.
    It is only by fixing on agreed meanings of terms in biomedical terminologies that we will be in a position to achieve that accumulation and integration of knowledge that is indispensable to progress at the frontiers of biomedicine. Standardly, the goal of fixing meanings is seen as being realized through the alignment of terms on what are called ‘concepts’. Part I addresses three versions of the concept-based approach – by Cimino, by Wüster, and by Campbell and associates – and (...)
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  50. Relations in Biomedical Ontologies.Barry Smith, Werner Ceusters, Bert Klagges, Jacob Köhler, Anand Kuma, Jane Lomax, Chris Mungall, , Fabian Neuhaus, Alan Rector & Cornelius Rosse - 2005 - Genome Biology 6 (5):R46.
    To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.
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