Linked Life Data

19584918

Source:http://linkedlifedata.com/resource/pubmed/id/19584918

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2009-7-8
pubmed:abstractText
Accurate prediction of survival rates of cancer patients is often key to stratify patients for prognosis and treatment. Survival prediction is often accomplished by the TNM system that involves only three factors: tumor extent, lymph node involvement, and metastasis. This prediction from the TNM has been limited, because other potential prognostic factors are not used in the system. Based on availability of large cancer datasets, it is possible to establish powerful prediction systems by using machine learning procedures and statistical methods. In this paper, we present an ensemble clustering-based approach to develop prognostic systems of cancer patients. Our method starts with grouping combinations that are formed using levels of factors recorded in the data. The dissimilarity measure between combinations is obtained through a sequence of data partitions produced by multiple use of PAM algorithm. This dissimilarity measure is then used with a hierarchical clustering method in order to find clusters of combinations. Prediction of survival is made simply by using the survival function derived from each cluster. Our approach admits multiple factors and provides a practical and useful tool in outcome prediction of cancer patients. A demonstration of use of the proposed method is given for lung cancer patients.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1110-7251
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
2009
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
632786
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Developing prognostic systems of cancer patients by ensemble clustering.
pubmed:affiliation
Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA. dchen@usuhs.mil
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.