Efficient Selection of Association Rules from Lymphedema Symptoms Data Using A Graph Structure
Identifieur interne : 002B51 ( Pmc/Curation ); précédent : 002B50; suivant : 002B52Efficient Selection of Association Rules from Lymphedema Symptoms Data Using A Graph Structure
Auteurs : Shuyu Xu ; Chi-Ren Shyu [États-Unis]Source :
- AMIA Annual Symposium Proceedings [ 1942-597X ] ; 2010.
Abstract
Secondary lymphedema (LE) is a chronic progressive disease often caused by cancer treatment, especially in patients who require surgical removal of or radiation to lymph nodes. While LE is incurable, it can be managed successfully with early detection and appropriate treatment. Detection and prediction of LE is difficult due to the absence of a “gold standard” for diagnosis. Despite this, management of the disease is accomplished through adherence to a set of guidelines developed by experts in the field. Unfortunately, not all the recommendations in such a document are supported by clear research evidence, and most of them are only based on expert judgment with limited evidence. This paper focuses on developing a new algorithm to extract specific association rules from LE survey data and efficiently index the rules for easy knowledge retrieval, with the ultimate goal discovering evidence-based and relevant knowledge for inclusion into the best practice document (BP) for the LE community.
Url:
PubMed: 21347111
PubMed Central: 3041337
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PMC:3041337Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>Secondary lymphedema (LE) is a chronic progressive disease often caused by cancer treatment, especially in patients who require surgical removal of or radiation to lymph nodes. While LE is incurable, it can be managed successfully with early detection and appropriate treatment. Detection and prediction of LE is difficult due to the absence of a “gold standard” for diagnosis. Despite this, management of the disease is accomplished through adherence to a set of guidelines developed by experts in the field. Unfortunately, not all the recommendations in such a document are supported by clear research evidence, and most of them are only based on expert judgment with limited evidence. This paper focuses on developing a new algorithm to extract specific association rules from LE survey data and efficiently index the rules for easy knowledge retrieval, with the ultimate goal discovering evidence-based and relevant knowledge for inclusion into the best practice document (BP) for the LE community.</p>
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<contrib-group><contrib contrib-type="author"><name><surname>Xu</surname>
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<fpage>912</fpage>
<lpage>916</lpage>
<permissions><copyright-statement>©2010 AMIA - All rights reserved.</copyright-statement>
<copyright-year>2010</copyright-year>
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<abstract><p>Secondary lymphedema (LE) is a chronic progressive disease often caused by cancer treatment, especially in patients who require surgical removal of or radiation to lymph nodes. While LE is incurable, it can be managed successfully with early detection and appropriate treatment. Detection and prediction of LE is difficult due to the absence of a “gold standard” for diagnosis. Despite this, management of the disease is accomplished through adherence to a set of guidelines developed by experts in the field. Unfortunately, not all the recommendations in such a document are supported by clear research evidence, and most of them are only based on expert judgment with limited evidence. This paper focuses on developing a new algorithm to extract specific association rules from LE survey data and efficiently index the rules for easy knowledge retrieval, with the ultimate goal discovering evidence-based and relevant knowledge for inclusion into the best practice document (BP) for the LE community.</p>
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