Efficient selection of association rules from lymphedema symptoms data using a graph structure.
Identifieur interne : 002736 ( PubMed/Corpus ); précédent : 002735; suivant : 002737Efficient selection of association rules from lymphedema symptoms data using a graph structure.
Auteurs : Shuyu Xu ; Chi-Ren ShyuSource :
- AMIA ... Annual Symposium proceedings. AMIA Symposium [ 1942-597X ] ; 2010.
English descriptors
- KwdEn :
- 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.
PubMed: 21347111
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pubmed:21347111Le document en format XML
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Annual Symposium proceedings. AMIA Symposium</title><idno type="eISSN">1942-597X</idno><imprint><date when="2010" type="published">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Breast Neoplasms (surgery)</term><term>Disease Management</term><term>Humans</term><term>Lymphedema</term></keywords><keywords scheme="MESH" qualifier="surgery" xml:lang="en"><term>Breast Neoplasms</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Disease Management</term><term>Humans</term><term>Lymphedema</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">21347111</PMID><DateCreated><Year>2011</Year><Month>02</Month><Day>24</Day></DateCreated><DateCompleted><Year>2016</Year><Month>04</Month><Day>22</Day></DateCompleted><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1942-597X</ISSN><JournalIssue CitedMedium="Internet"><Volume>2010</Volume><PubDate><Year>2010</Year><Month>Nov</Month><Day>13</Day></PubDate></JournalIssue><Title>AMIA ... Annual Symposium proceedings. AMIA Symposium</Title><ISOAbbreviation>AMIA Annu Symp Proc</ISOAbbreviation></Journal><ArticleTitle>Efficient selection of association rules from lymphedema symptoms data using a graph structure.</ArticleTitle><Pagination><MedlinePgn>912-6</MedlinePgn></Pagination><Abstract><AbstractText>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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Xu</LastName><ForeName>Shuyu</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Informatics Institute.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shyu</LastName><ForeName>Chi-Ren</ForeName><Initials>CR</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 NR005342</GrantID><Acronym>NR</Acronym><Agency>NINR NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01 NR010293</GrantID><Acronym>NR</Acronym><Agency>NINR NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2010</Year><Month>11</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>AMIA Annu Symp Proc</MedlineTA><NlmUniqueID>101209213</NlmUniqueID><ISSNLinking>1559-4076</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Cochrane Database Syst Rev. 2004;(4):CD003141</RefSource><PMID Version="1">15495042</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Lymphat Res Biol. 2005;3(4):208-17</RefSource><PMID Version="1">16379589</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Lymphoedema. 2008 Oct 1;3(2):38-44</RefSource><PMID Version="1">20657749</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D001943" MajorTopicYN="Y">Breast Neoplasms</DescriptorName><QualifierName UI="Q000601" MajorTopicYN="N">surgery</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D019468" MajorTopicYN="N">Disease Management</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008209" MajorTopicYN="Y">Lymphedema</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC3041337</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2011</Year><Month>2</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2011</Year><Month>2</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>4</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">21347111</ArticleId><ArticleId IdType="pmc">PMC3041337</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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