LymphedemaV1, Pmc, Corpus, bibRecord, 003387

Using Temporal Mining to Examine the Development of Lymphedema in Breast Cancer Survivors

Identifieur interne : 003387 ( Pmc/Corpus ); précédent : 003386; suivant : 003388

Using Temporal Mining to Examine the Development of Lymphedema in Breast Cancer Survivors

Auteurs : Jason M. Green ; Sowjanya Paladugu ; Xu Shuyu ; Bob R. Stewart ; Chi-Ren Shyu ; Jane M. Armer

Source :

Nursing research [ 0029-6562 ] ; 2013.

RBID : PMC:4526254

Abstract

Background

Secondary lymphedema is a lifetime risk for breast cancer survivors and can severely affect quality of life. Early detection and treatment are crucial for successful lymphedema management. Limb volume measurements can be utilized not only to diagnose lymphedema but also to track progression of limb volume changes before lymphedema, which has the potential to provide insight into the development of this condition.

Objectives

To identify commonly occurring patterns in limb volumes changes in breast cancer survivors before the development of lymphedema, and to determine if there were differences in these patterns between certain patient subgroups. Furthermore, pattern differences were studied between patients who developed lymphedema quickly and those whose onset was delayed.

Method

A temporal data mining technique was used to identify and compare common patterns in limb volume measurements in patient subgroups of study participants (n = 232). Patterns were filtered initially by support and confidence values; then t-tests were used to determine statistical significance of the remaining patterns.

Results

Higher body mass index and the presence of postoperative swelling are supported as risk factors for lymphedema. In addition, a difference in trajectory to the lymphedema state was observed.

Discussion

The results have potential to guide clinical guidelines for assessment of latent and early-onset lymphedema.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4526254

DOI: 10.1097/NNR.0b013e318283da67
PubMed: 23458909
PubMed Central: 4526254

Links to Exploration step

PMC:4526254

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Using Temporal Mining to Examine the Development of Lymphedema in Breast Cancer Survivors</title>
<author><name sortKey="Green, Jason M" sort="Green, Jason M" uniqKey="Green J" first="Jason M." last="Green">Jason M. Green</name>
</author>
<author><name sortKey="Paladugu, Sowjanya" sort="Paladugu, Sowjanya" uniqKey="Paladugu S" first="Sowjanya" last="Paladugu">Sowjanya Paladugu</name>
</author>
<author><name sortKey="Shuyu, Xu" sort="Shuyu, Xu" uniqKey="Shuyu X" first="Xu" last="Shuyu">Xu Shuyu</name>
</author>
<author><name sortKey="Stewart, Bob R" sort="Stewart, Bob R" uniqKey="Stewart B" first="Bob R." last="Stewart">Bob R. Stewart</name>
</author>
<author><name sortKey="Shyu, Chi Ren" sort="Shyu, Chi Ren" uniqKey="Shyu C" first="Chi-Ren" last="Shyu">Chi-Ren Shyu</name>
</author>
<author><name sortKey="Armer, Jane M" sort="Armer, Jane M" uniqKey="Armer J" first="Jane M." last="Armer">Jane M. Armer</name>
</author>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Using Temporal Mining to Examine the Development of Lymphedema in Breast Cancer Survivors</title>
<author><name sortKey="Green, Jason M" sort="Green, Jason M" uniqKey="Green J" first="Jason M." last="Green">Jason M. Green</name>
</author>
<author><name sortKey="Paladugu, Sowjanya" sort="Paladugu, Sowjanya" uniqKey="Paladugu S" first="Sowjanya" last="Paladugu">Sowjanya Paladugu</name>
</author>
<author><name sortKey="Shuyu, Xu" sort="Shuyu, Xu" uniqKey="Shuyu X" first="Xu" last="Shuyu">Xu Shuyu</name>
</author>
<author><name sortKey="Stewart, Bob R" sort="Stewart, Bob R" uniqKey="Stewart B" first="Bob R." last="Stewart">Bob R. Stewart</name>
</author>
<author><name sortKey="Shyu, Chi Ren" sort="Shyu, Chi Ren" uniqKey="Shyu C" first="Chi-Ren" last="Shyu">Chi-Ren Shyu</name>
</author>
<author><name sortKey="Armer, Jane M" sort="Armer, Jane M" uniqKey="Armer J" first="Jane M." last="Armer">Jane M. Armer</name>
</author>
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<series><title level="j">Nursing research</title>
<idno type="ISSN">0029-6562</idno>
<idno type="eISSN">1538-9847</idno>
<imprint><date when="2013">2013</date>
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<front><div type="abstract" xml:lang="en"><sec id="S1"><title>Background</title>
<p id="P1">Secondary lymphedema is a lifetime risk for breast cancer survivors and can severely affect quality of life. Early detection and treatment are crucial for successful lymphedema management. Limb volume measurements can be utilized not only to diagnose lymphedema but also to track progression of limb volume changes before lymphedema, which has the potential to provide insight into the development of this condition.</p>
</sec>
<sec id="S2"><title>Objectives</title>
<p id="P2">To identify commonly occurring patterns in limb volumes changes in breast cancer survivors before the development of lymphedema, and to determine if there were differences in these patterns between certain patient subgroups. Furthermore, pattern differences were studied between patients who developed lymphedema quickly and those whose onset was delayed.</p>
</sec>
<sec id="S3"><title>Method</title>
<p id="P3">A temporal data mining technique was used to identify and compare common patterns in limb volume measurements in patient subgroups of study participants (<italic>n</italic>
 = 232). Patterns were filtered initially by support and confidence values; then t-tests were used to determine statistical significance of the remaining patterns.</p>
</sec>
<sec id="S4"><title>Results</title>
<p id="P4">Higher body mass index and the presence of postoperative swelling are supported as risk factors for lymphedema. In addition, a difference in trajectory to the lymphedema state was observed.</p>
</sec>
<sec id="S5"><title>Discussion</title>
<p id="P5">The results have potential to guide clinical guidelines for assessment of latent and early-onset lymphedema.</p>
</sec>
</div>
</front>
</TEI>
<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
  <pmc-dir>properties manuscript</pmc-dir>
  <front><journal-meta><journal-id journal-id-type="nlm-journal-id">0376404</journal-id>
<journal-id journal-id-type="pubmed-jr-id">6154</journal-id>
<journal-id journal-id-type="nlm-ta">Nurs Res</journal-id>
<journal-id journal-id-type="iso-abbrev">Nurs Res</journal-id>
<journal-title-group><journal-title>Nursing research</journal-title>
</journal-title-group>
<issn pub-type="ppub">0029-6562</issn>
<issn pub-type="epub">1538-9847</issn>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">23458909</article-id>
<article-id pub-id-type="pmc">4526254</article-id>
<article-id pub-id-type="doi">10.1097/NNR.0b013e318283da67</article-id>
<article-id pub-id-type="manuscript">NIHMS437673</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Using Temporal Mining to Examine the Development of Lymphedema in Breast Cancer Survivors</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Green</surname>
<given-names>Jason M.</given-names>
</name>
<degrees>PhD</degrees>
<aff id="A1">Graduate Research Assistant, Department of Computer Science, University of Missouri, Columbia, Missouri</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Paladugu</surname>
<given-names>Sowjanya</given-names>
</name>
<degrees>MS</degrees>
<aff id="A2">Graduate Research Assistant, Department of Computer Science, University of Missouri, Columbia, Missouri</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Shuyu</surname>
<given-names>Xu</given-names>
</name>
<degrees>MS</degrees>
<aff id="A3">Graduate Research Assistant, Informatics Institute, University of Missouri, Columbia, Missouri</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Stewart</surname>
<given-names>Bob R.</given-names>
</name>
<degrees>EdD</degrees>
<aff id="A4">Professor Emeritus, College of Education and Adjunct Clinical Faculty, Sinclair School of Nursing, University of Missouri, Columbia, Missouri</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Shyu</surname>
<given-names>Chi-Ren</given-names>
</name>
<degrees>PhD</degrees>
<aff id="A5">Professor and Director of the Informatics Institute, Department of Computer Science, Informatics Institute, University of Missouri, Columbia, Missouri</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Armer</surname>
<given-names>Jane M.</given-names>
</name>
<degrees>PhD, RN, FAAN</degrees>
<aff id="A6">Professor and Director of Nursing Research, Ellis Fischel Cancer Center and Director, American Lymphedema Framework Project, Sinclair School of Nursing, University of Missouri, Columbia, Missouri</aff>
</contrib>
</contrib-group>
<author-notes><corresp id="FN1">Correspondence: Jane Armer, DC 116.05 Suite 408, Ellis Fischel Cancer Center, 115 Business Loop 70 W. Columbia, MO 65203, <email>armer@missouri.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="nihms-submitted"><day>28</day>
<month>7</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="ppub"><season>Mar-Apr</season>
<year>2013</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>05</day>
<month>8</month>
<year>2015</year>
</pub-date>
<volume>62</volume>
<issue>2</issue>
<fpage>122</fpage>
<lpage>129</lpage>
<pmc-comment>elocation-id from pubmed: 10.1097/NNR.0b013e318283da67</pmc-comment>
      <abstract><sec id="S1"><title>Background</title>
<p id="P1">Secondary lymphedema is a lifetime risk for breast cancer survivors and can severely affect quality of life. Early detection and treatment are crucial for successful lymphedema management. Limb volume measurements can be utilized not only to diagnose lymphedema but also to track progression of limb volume changes before lymphedema, which has the potential to provide insight into the development of this condition.</p>
</sec>
<sec id="S2"><title>Objectives</title>
<p id="P2">To identify commonly occurring patterns in limb volumes changes in breast cancer survivors before the development of lymphedema, and to determine if there were differences in these patterns between certain patient subgroups. Furthermore, pattern differences were studied between patients who developed lymphedema quickly and those whose onset was delayed.</p>
</sec>
<sec id="S3"><title>Method</title>
<p id="P3">A temporal data mining technique was used to identify and compare common patterns in limb volume measurements in patient subgroups of study participants (<italic>n</italic>
 = 232). Patterns were filtered initially by support and confidence values; then t-tests were used to determine statistical significance of the remaining patterns.</p>
</sec>
<sec id="S4"><title>Results</title>
<p id="P4">Higher body mass index and the presence of postoperative swelling are supported as risk factors for lymphedema. In addition, a difference in trajectory to the lymphedema state was observed.</p>
</sec>
<sec id="S5"><title>Discussion</title>
<p id="P5">The results have potential to guide clinical guidelines for assessment of latent and early-onset lymphedema.</p>
</sec>
</abstract>
<kwd-group><kwd>secondary lymphedema</kwd>
<kwd>breast cancer</kwd>
<kwd>temporal analysis</kwd>
<kwd>data mining</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>

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Using Temporal Mining to Examine the Development of Lymphedema in Breast Cancer Survivors

Using Temporal Mining to Examine the Development of Lymphedema in Breast Cancer Survivors

Source :

Abstract

Links to Exploration step

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