A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/ftp/arxiv/papers/1107/1107.4651.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Higher Order Programming to Mine Knowledge for a Modern Medical Expert System
[article]
<span title="2011-07-23">2011</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
Preserved Fulltext
Knowledge mining is the process of deriving new and useful knowledge from vast volumes of data and background knowledge. Modern healthcare organizations regularly generate huge amount of electronic data stored in the databases. These data are a valuable resource for mining useful knowledge to help medical practitioners making appropriate and accurate decision on the diagnosis and treatment of diseases. In this paper, we propose the design of a novel medical expert system based on a
<span class="external-identifiers">
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1107.4651v1">arXiv:1107.4651v1</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/midttzcevbddplkaqogp2e6vcq">fatcat:midttzcevbddplkaqogp2e6vcq</a>
</span>
more »
... ming framework. The proposed system includes a knowledge-mining component as a repertoire of tools for discovering useful knowledge. The implementation of classification and association mining tools based on the higher order and meta-level programming schemes using Prolog has been presented to express the power of logic-based language. Such language also provides a pattern matching facility, which is an essential function for the development of knowledge-intensive tasks. Besides the major goal of medical decision support, the knowledge discovered by our logic-based knowledge-mining component can also be deployed as background knowledge to pre-treatment data from other sources as well as to guard the data repositories against constraint violation. A framework for knowledge deployment is also presented.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201001032035/https://arxiv.org/ftp/arxiv/papers/1107/1107.4651.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/9d/e1/9de1df0fa56e4326d7e15ce95a77998ed37bae2f.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1107.4651v1" title="arxiv.org access">
<button class="ui compact blue labeled icon button serp-button">
<i class="file alternate outline icon"></i>
arxiv.org
</button>
</a>