LymphedemaV1, Main, Exploration, bibRecord, 005929

Efficient Selection of Association Rules from Lymphedema Symptoms Data Using A Graph Structure

Identifieur interne : 005929 ( Main/Exploration ); précédent : 005928; suivant : 005930

Efficient 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.

RBID : PMC:3041337

Descripteurs français

KwdFr :
- Humains, Lymphoedème, Prise en charge de la maladie, Tumeurs du sein ().
MESH :
- Humains, Lymphoedème, Prise en charge de la maladie, Tumeurs du sein.

English descriptors

KwdEn :
- Breast Neoplasms (surgery), Disease Management, Humans, Lymphedema.
MESH :
- surgery : Breast Neoplasms.
- Disease Management, Humans, Lymphedema.

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:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041337

PubMed: 21347111
PubMed Central: 3041337

Affiliations:

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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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Serveur d'exploration sur le lymphœdème

Efficient Selection of Association Rules from Lymphedema Symptoms Data Using A Graph Structure

Efficient Selection of Association Rules from Lymphedema Symptoms Data Using A Graph Structure

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki

	Serveur d'exploration sur le lymphœdème
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