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DOI: 10.4230/DagSemProc.05171.4
Modelling and Implementing a Knowledge Base for Checking Medical Invoices with DLV

Authors: Gabriele Kern-Isberner, Christoph Beierle, and Oliver Dusso

Published in: Dagstuhl Seminar Proceedings, Volume 5171, Nonmonotonic Reasoning, Answer Set Programming and Constraints (2005)

Abstract
Checking medical invoices, done by every health insurance company, is a labor-intensive task. Both speed and quality of executing this task may be increased by the knowledge-based decision support system ACMI which we present in this paper. As the relevant regulations also contain various default rules, ACMI`s knowledge core is modelled using the answer set programming paradigm. It turned out that all relevant rules could be expressed directly in this framework, providing for a declarative and easily extendable and modifiable knowledge base. ACMI is implemented using the DLV system.
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Gabriele Kern-Isberner, Christoph Beierle, and Oliver Dusso. Modelling and Implementing a Knowledge Base for Checking Medical Invoices with DLV. In Nonmonotonic Reasoning, Answer Set Programming and Constraints. Dagstuhl Seminar Proceedings, Volume 5171, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{kernisberner_et_al:DagSemProc.05171.4,
  author =	{Kern-Isberner, Gabriele and Beierle, Christoph and Dusso, Oliver},
  title =	{{Modelling and Implementing a Knowledge Base for Checking Medical Invoices with DLV}},
  booktitle =	{Nonmonotonic Reasoning, Answer Set Programming and Constraints},
  pages =	{1--12},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{5171},
  editor =	{Gerhard Brewka and Ilkka Niemel\"{a} and Torsten Schaub and Miroslaw Truszczynski},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.05171.4},
  URN =		{urn:nbn:de:0030-drops-2610},
  doi =		{10.4230/DagSemProc.05171.4},
  annote =	{Keywords: Answer sets, default rules, health insurance, rule schemas}
}
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