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  • 1.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Krusinska, Ewa
    IMT LiU.
    Strömberg, Jan-Erik
    Dept Electrical Engineering LiU.
    Extraction of diagnostic rules using recursive partitioning systems: A comparision of two approaches1992In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 4, p. 373-387Article in journal (Refereed)
  • 2.
    Dentler, Kathrin
    et al.
    Vrije University of Amsterdam, Netherlands; University of Amsterdam, Netherlands.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. University of Amsterdam, Netherlands.
    Intra-axiom redundancies in SNOMED CT2015In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 65, no 1, p. 29-34Article in journal (Refereed)
    Abstract [en]

    Objective: Intra-axiom redundancies are elements of concept definitions that are redundant as they are entailed by other elements of the concept definition. While such redundancies are harmless from a logical point of view, they make concept definitions hard to maintain, and they might lead to content-related problems when concepts evolve. The objective of this study is to develop a fully automated method to detect intra-axiom redundancies in OWL 2 EL and apply it to SNOMED Clinical Terms (SNOMED CT). Materials and methods: We developed a software program in which we implemented, adapted and extended readily existing rules for redundancy elimination. With this, we analysed occurence of redundancy in 11 releases of SNOMED CT(January 2009 to January 2014). We used the ELK reasoner to classify SNOMED CT, and Pellet for explanation of equivalence. We analysed the completeness and soundness of the results by an in-depth examination of the identified redundant elements in the July 2012 release of SNOMED CT. To determine if concepts with redundant elements lead to maintenance issues, we analysed a small sample of solved redundancies. Results: Analyses showed that the amount of redundantly defined concepts in SNOMED CT is consistently around 35,000. In the July 2012 version of SNOMED CT, 35,010(12%) of the 296,433 concepts contained redundant elements in their definitions. The results of applying our method are sound and complete with respect to our evaluation. Analysis of solved redundancies suggests that redundancies in concept definitions lead to inadequate maintenance of SNOMED CT. Conclusions: Our analysis revealed that redundant elements are continuously introduced and removed, and that redundant elements may be overlooked when concept definitions are corrected. Applying our redundancy detection method to remove intra-axiom redundancies from the stated form of SNOMED CT and to point knowledge modellers to newly introduced redundancies can support creating and maintaining a redundancy-free version of SNOMED CT. (C) 2014 Elsevier B.V. All rights reserved.

  • 3.
    Lillehaug, Svein-Ivar
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lajoie, Susanne
    McGill University,Montreal .
    AI in medical education - another grand challenge for medical informatics1998In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 12, p. 197-225Article in journal (Refereed)
  • 4.
    Shahsavar, Nosrat
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ludwigs, Ulf
    Södersjukhuset Stockholm.
    Blomqvist, Hans
    Danderyds sjukhus .
    Gill, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Matell, George
    Södersjukhuset Stockholm.
    Evaluation of a knowledge-based decision-support system for ventilator therapy management1995In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 7, p. 37-52Article in journal (Refereed)
  • 5.
    Strömberg, Jan-Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Babić, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Krusinska, Ewa
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Extraction of Diagnostic Rules using Recursive Partitioning Systems: A Comparision of Two Approaches1992In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 4, no 5, p. 373-387Article in journal (Refereed)
    Abstract [en]

    There are several empirical systems based on principles of learning from examples that can be used as a tool for decision support by medical experts in medicine. We are comparing two systems of this kind, one based on Quinlan's ID3 algorithm, and the other based on Breiman's CART (Classification And Regression Trees) algorithm. Both of these methods represent the extracted knowledge in form of binary tree structured diagnostic rules. In this paper we present the most important features of the two systems and discuss important differences between the two; all this in a uniform framework. We then study the implications these differences and similarities make when applied to clinical data. The empirical study includes two medical data sets: the first one concerning patients with highly selective vagotomy (HSV) for duodenal ulcer surgery, and the second one concerning patients with non-specified liver disease.

  • 6.
    Timpka, Toomas
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Division of Preventive and Social Medicine and Public Health Science. Östergötlands Läns Landsting, FHVC - Folkhälsovetenskapligt centrum.
    Proactive health computing2001In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 23, no 1, p. 13-24Article in journal (Refereed)
    Abstract [en]

    In an analysis departing from the global health situation, the foundation for a change of paradigm in health informatics based on socially embedded information infrastructures and technologies is identified and discussed. It is shown how an increasing computing and data transmitting capacity can be employed for proactive health computing. As a foundation for ubiquituos health promotion and prevention of disease and injury, proactive health systems use data from multiple sources to supply individuals and communities evidence-based information on means to improve their state of health and avoid health risks. The systems are characterised by:being profusely connected to the world around them, using perceptual interfaces, sensors and actuators,responding to external stimuli at faster than human speeds,networked feed-back loops, andhumans remaining in control, while being left outside the primary computing loop.The extended scientific mission of this new partnership between computer science, electrical engineering and social medicine is suggested to be the investigation of how the dissemination of information and communication technology on democratic grounds can be made even more important for global health than sanitation and urban planning became a century ago. Copyright © 2001 Elsevier Science B.V.

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