Applying Data Mining Techniques to a Health Insurance Information System

Summary: Data mining on large health-insurance data (claims, GP records) to reveal unknown patterns. Novel integration of association rules on episode data with cross-database neural segmentation, enabling GP clustering by practice style and discovery of pathology patterns beyond conventional methods. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: he962f92c96ad2d92
Venue: VLDB
Year: 1996
Pagerank: 4.9793485e-05
Overall Rank: 13,273 | 10.76%
DOI: -

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Marisa S. Viveros (IBM)
2. John P. Nearhos (Health Insurance Commission)
3. Michael J. Rothman (IBM)

BibTeX Citation

@article{viveros_vldb96,
        title = {{Applying Data Mining Techniques to a Health Insurance Information System}},
        author = {Viveros, Marisa S. and Nearhos, John P. and Rothman, Michael J.},
        journal = {PVLDB},
        series = {{VLDB} '96},
        pages = {286},
        year = {1996}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
13	Mining Association Rules between Sets of Items in Large Databases	1993	SIGMOD	0.00064420972
29	Fast Algorithms for Mining Association Rules	1994	VLDB	0.0005121339

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