Applying Data Mining Techniques to a Health Insurance Information System.

Marisa S. Viveros, John P. Nearhos, Michael J. Rothman: Applying Data Mining Techniques to a Health Insurance Information System. VLDB 1996: 286-294

@inproceedings{DBLP:conf/vldb/ViverosNR96,
  author    = {Marisa S. Viveros and
               John P. Nearhos and
               Michael J. Rothman},
  editor    = {T. M. Vijayaraman and
               Alejandro P. Buchmann and
               C. Mohan and
               Nandlal L. Sarda},
  title     = {Applying Data Mining Techniques to a Health Insurance Information
               System},
  booktitle = {VLDB'96, Proceedings of 22th International Conference on Very
               Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India},
  publisher = {Morgan Kaufmann},
  year      = {1996},
  isbn      = {1-55860-382-4},
  pages     = {286-294},
  ee        = {db/conf/vldb/ViverosNR96.html},
  crossref  = {DBLP:conf/vldb/96},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

This paper addresses the effectiveness of two data mining techniques in analyzing and retrieving unknown behaviour patterns from gigabytes of data collected in the health insurance industry. Specifically, an episode (claims) database for pathology services and a general practitioners database were used. Association rules were applied to the episode database; neural segmentation was applied to the overlaying of both databases. The results obtained from this study demonstrate the potential value of data mining in health insurance information systems, by detecting patterns in the ordering of pathology services and by classifying general practitioners into groups reflecting the nature and stlye of their practices. The approach used led to results which could not have been obtained using conventional techniques.

Copyright © 1996 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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T. M. Vijayaraman, Alejandro P. Buchmann, C. Mohan, Nandlal L. Sarda (Eds.): VLDB'96, Proceedings of 22th International Conference on Very Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India. Morgan Kaufmann 1996, ISBN 1-55860-382-4
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References

[AIS93]: Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216
[AS94]: Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499
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