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Determining Pattern Similarity in a Medical Recommender System

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Data and Knowledge Engineering (ICDKE 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7696))

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Abstract

As recommender systems have proven their effectiveness in other areas, it is aimed to transfer this approach for use in medicine. Particularly, the diagnoses of physicians made in rural hospitals of developing countries, in remote areas or in situations of uncertainty are to be complemented by machine recommendations drawing on large bases of expert knowledge in order to reduce the risk to patients. Recommendation is mainly based on finding known patterns similar to a case under consideration. To search for such patterns in rather large databases, a weighted similarity distance is employed, which is specially derived for medical knowledge. For collaborative filtering an incremental algorithm, called W-InCF, is used working with the Mahalanobis distance and fuzzy membership. W-InCF consists of a learning phase, in which a cluster model of patients’ medical history is constructed incrementally, and a prediction phase, in which the medical pattern of each patient considered is compared with the model to determine the most similar cluster. Fuzzy sets are employed to cope with possible confusion of decision making on overlapping clusters. The degrees of membership to these fuzzy sets is expressed by a weighted Mahalanobis radial basis function, and the weights are derived from risk factors identified by experts. The algorithm is validated using data on cephalopelvic disproportion.

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Author information

Authors and Affiliations

  1. Chair of Computer Engineering, Fernuniversität in Hagen, Germany

    Maytiyanin Komkhao

  2. School of Software, Faculty of Engineering and Information Technology, University of Technology Sydney, P.O. Box 123, Broadway, NSW, 2007, Australia

    Jie Lu

  3. School of Information Engineering, Guangdong University of Technology, 100, Waihuan Xi Road, Panyu District, Guangzhou, P.R. China

    Lichen Zhang

Authors
  1. Maytiyanin Komkhao
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  2. Jie Lu
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  3. Lichen Zhang
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Editor information

Editors and Affiliations

  1. School of Information Technology, Deakin University, Melbourne Burwood Campus, 221 Burwood Highway, 3125, Burwood, VIC, Australia

    Yang Xiang

  2. Media Distribution, Telstra Corporation Limited, 21/35 Collins St, 3000, Melbourne, VIC, Australia

    Mukaddim Pathan

  3. Department of Mathematics and Computing, The University of Southern Queensland, Toowoomba, QLD, Australia

    Xiaohui Tao

  4. Department of Mathematics and Computing, The University of Southern Queensland, Toowoomba, QLD, Australia

    Hua Wang

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© 2012 Springer-Verlag Berlin Heidelberg

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Komkhao, M., Lu, J., Zhang, L. (2012). Determining Pattern Similarity in a Medical Recommender System. In: Xiang, Y., Pathan, M., Tao, X., Wang, H. (eds) Data and Knowledge Engineering. ICDKE 2012. Lecture Notes in Computer Science, vol 7696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34679-8_11

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  • DOI: https://doi.org/10.1007/978-3-642-34679-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34678-1

  • Online ISBN: 978-3-642-34679-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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