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Context-Dependent Interpretation of Medical Data

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Recent Developments and New Directions in Soft Computing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 317))

Abstract

Medical data are intrinsically context-dependent, and cannot be properly interpreted outside of their specific contexts. Therefore, data analysis, especially, secondary data analysis, such as data mining, must incorporate contextual information. This chapter discusses the need for an explicit context representation in medical data mining. It focuses on five contextual dimensions: goal orientation, interdependency of data, time sensitivity, source validity, and absent value semantics. It demonstrates context-dependent modeling based on examples of clinical data used for screening, diagnosis, and research of a serious respiratory disorder, obstructive sleep apnea (OSA). In particular, the chapter describes context-dependent interpretation for three OSA risk factors: large neck circumference, snoring, and smoking. Furthermore, it presents a conceptual framework for representation of the contextual information. This framework is based on a semiotic approach to represent multiple interpretations of data and a fuzzy-logic approach to represent vagueness of data.

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Author information

Authors and Affiliations

  1. Department of Computing Science, Thompson Rivers University, Kamloops, BC, Canada

    M. Kwiatkowska

  2. Department of Medicine, University of British Columbia, Vancouver, BC, Canada

    N. T. Ayas

Authors
  1. M. Kwiatkowska
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  2. N. T. Ayas
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Corresponding author

Correspondence to M. Kwiatkowska .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  2. Minister of Communications and Information Technologies of the Republic of Azerbaijan, Baku, Azerbaijan

    Ali M. Abbasov

  3. Iona College, Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

  4. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  5. Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Z. Reformat

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Kwiatkowska, M., Ayas, N.T. (2014). Context-Dependent Interpretation of Medical Data. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Directions in Soft Computing. Studies in Fuzziness and Soft Computing, vol 317. Springer, Cham. https://doi.org/10.1007/978-3-319-06323-2_26

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  • DOI: https://doi.org/10.1007/978-3-319-06323-2_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06322-5

  • Online ISBN: 978-3-319-06323-2

  • eBook Packages: EngineeringEngineering (R0)

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