Developing prognostic systems of cancer patients by ensemble clustering.
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...
| Publicado en: | Journal of Biomedicine & Biotechnology pp. 7p - 8 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2009 Regular Issue
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105438733&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105438733 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2009 Regular Issue pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105438733 2010398049 10.1155/2009/632786 NLM19584918 105438733 ppf: 7p ppct: 1 formats: fmt: @attributes: type: P tig: atl: Developing prognostic systems of cancer patients by ensemble clustering. aug: au: Chen D Xing K Henson D Sheng L Schwartz AM Cheng X affil: Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA. dchen@usuhs.mil sug: subj: Cancer Patients Cluster Analysis Neoplasms Prognosis Prognosis Methods Algorithms Data Analysis Data Collection Forecasting Funding Source Learning Lung Neoplasms Lymph Nodes Neoplasm Metastasis Statistics Treatment Outcomes Human ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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