Conference proceeding
Clustering Cancer Data by Areas between Survival Curves
2016 IEEE FIRST INTERNATIONAL CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES (CHASE), pp 61-66
01 Jan 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We propose a hierarchical clustering method for prognostic clustering of cancer patients. Dissimilarity between two subsets of patients is defined as the area between two corresponding Kaplan-Meier curves. The proposed method is applied to the breast cancer data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute and compared with the linkage approach. The proposed method is convenient to use and can generate dendrograms compatible with those from the linkage approach.
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Details
- Title
- Clustering Cancer Data by Areas between Survival Curves
- Creators
- Dechang Chen - Uniformed Services University of the Health SciencesHuan Wang - George Washington UniversityDonald E. Henson - Uniformed Services University of the Health SciencesLi Sheng - Drexel UniversityMatthew T. Hueman - Walter Reed National Military Medical CenterArnold M. Schwartz - George Washington UniversityIEEE
- Publication Details
- 2016 IEEE FIRST INTERNATIONAL CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES (CHASE), pp 61-66
- Conference
- 2016 IEEE FIRST INTERNATIONAL CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES (CHASE), 1st
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000389604100011
- Scopus ID
- 2-s2.0-84987664795
- Other Identifier
- 991019168839704721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Biomedical
- Medical Informatics