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The Elicitation, Representation, Application, and Automated Discovery of Time-Oriented Declarative Clinical Knowledge

  • Conference paper
Process Support and Knowledge Representation in Health Care (ProHealth 2012, KR4HC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7738))

Abstract

Monitoring, interpretation, and analysis of large amounts of time-stamped clinical data are tasks that are at the core of tasks such as the management of chronic patients using clinical guidelines, the retrospective assessment of the quality of that application, or the related task of clinical research by learning new knowledge from the accumulating data. I briefly describe several conceptual and computational architectures developed over the past 20 years, mostly by my research teams at Stanford and Ben Gurion universities, for knowledge-based performance of these tasks, and highlight the complex and interesting relationships amongst them. Examples of such architectures include the IDAN goal-directed temporal-mediation and the Momentum data-driven monitoring architectures, both of which are based on the knowledge-based temporal-abstraction method; the KNAVE-II and VISITORS knowledge-based interactive-exploration frameworks for single and multiple longitudinal records; and the KarmaLego interval-based temporal data mining methodology. I point out the progression from individual-subject data-interpretation, monitoring, and therapy, to multiple-patient aggregate analysis and research, and finally to the discovery and learning of new knowledge. This progression can be viewed as a positive-feedback loop, in which new knowledge is brought back to bear upon both individual-patient management and on the learning of new and meaningful (temporal) associations.

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Authors and Affiliations

  1. Medical Informatics Research Center, Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel

    Yuval Shahar

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  1. Yuval Shahar
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Editors and Affiliations

  1. Dept. of Computer Science, Data Management, University of Erlangen and Nuremberg, Martensstr. 3, 91058, Erlangen, Germany

    Richard Lenz

  2. Institute of Software Technology and Interactive Systems, Information and Software Engineering Gruop, Vienna University of Technology, Favoritenstr. 9-11, 1040, Vienna, Austria

    Silvia Miksch

  3. Faculty of Social Sciences, Dept. of Information Systems, University of Haifa, Rabin Bldg., room 7049, 31905, Haifa, Israel

    Mor Peleg

  4. Institute of Databases and Information Systems, Ulm University, Building 027 # room 524, James-Tranck-Ring, 89069, Ulm, Germany

    Manfred Reichert

  5. Dept. d’Enginyeria Informàtica i Matimàtiques, Research Group on Artificial Intelligence, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007, Tarragona, Spain

    David Riaño

  6. Dept. of AI, Knowledge Representation and Reasoning Gruop, Vrije Universiteit Amsterdam, De Boelelaan 1081A, 1081HV, Amsterdam, The Netherlands

    Annette ten Teije

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Shahar, Y. (2013). The Elicitation, Representation, Application, and Automated Discovery of Time-Oriented Declarative Clinical Knowledge. In: Lenz, R., Miksch, S., Peleg, M., Reichert, M., Riaño, D., ten Teije, A. (eds) Process Support and Knowledge Representation in Health Care. ProHealth KR4HC 2012 2012. Lecture Notes in Computer Science(), vol 7738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36438-9_1

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  • DOI: https://doi.org/10.1007/978-3-642-36438-9_1

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