loading...
Caffeine Intake, Race, and Risk of Invasive Breast Cancer Lessons Learned from Data Mining a Clinical Database
Salt Lake City, Utah June 22-June 23
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CBMS.2006.6419th IEEE Symposium on Computer-Based ...
 This Article 
 
Buy PURCHASE ARTICLE: $19
PDF
HTML
Buy IEEE Xplore Subscribers
 
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
ASCII Text
x 
 
Susan Maskery, Yonghong Zhang, Hai Hu, Craig Shriver, Jeffrey Hooke, Michael Liebman, "Caffeine Intake, Race, and Risk of Invasive Breast Cancer Lessons Learned from Data Mining a Clinical Database," Computer-Based Medical Systems, IEEE Symposium on, pp. 714-718, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006.
 
 
BibTex
x
 
@article{ 10.1109/CBMS.2006.64,
author = {Susan Maskery and Yonghong Zhang and Hai Hu and Craig Shriver and Jeffrey Hooke and Michael Liebman},
title = {Caffeine Intake, Race, and Risk of Invasive Breast Cancer Lessons Learned from Data Mining a Clinical Database},
journal ={Computer-Based Medical Systems, IEEE Symposium on},
volume = {0},
year = {2006},
issn = {1063-7125},
pages = {714-718},
doi = {http://doi.ieeecomputersociety.org/10.1109/CBMS.2006.64},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer-Based Medical Systems, IEEE Symposium on
TI - Caffeine Intake, Race, and Risk of Invasive Breast Cancer Lessons Learned from Data Mining a Clinical Database
SN - 1063-7125
SP714
EP718
A1 - Susan Maskery,
A1 - Yonghong Zhang,
A1 - Hai Hu,
A1 - Craig Shriver,
A1 - Jeffrey Hooke,
A1 - Michael Liebman,
PY - 2006
KW - null
VL - 0
JA - Computer-Based Medical Systems, IEEE Symposium on
ER -
 
Susan Maskery, Windber Research Institute, USA
Yonghong Zhang, Windber Research Institute, USA
Hai Hu, Windber Research Institute, USA
Craig Shriver, Walter Reed Army Medical Center, USA
Jeffrey Hooke, Walter Reed Army Medical Center, USA
Michael Liebman, Windber Research Institute, USA
Over the past five years the Clinical Breast Care Project (CBCP) has amassed a significant patient database and tissue repository related to breast disease and breast cancer. We have begun mining this unique data source (i.e. life history questionnaire data, pathology reports, analysis of blood and tissue samples) to examine interactions between known risk factors for breast cancer development (i.e. menopausal status, parity, etc.) with breast disease and cancer incidence in our patient population. From these initial forays into analyzing the CBCP?s data repository, we have begun to develop protocols for data mining. In particular, a crucial first step is to quantify interactions between variables of interest prior to any specific significance tests relating individual variables to risk of a clinical result. For this purpose, we find Bayesian network analysis the most useful method for exploration of data interactions. To illustrate this point, this abstract details an investigation into the effect of caffeine consumption on breast cancer incidence in our CBCP population. Based on our experience with this and other studies we strongly recommend Bayesian network analysis of all variables of interest as an initial data exploration tool.
Citation:
Susan Maskery, Yonghong Zhang, Hai Hu, Craig Shriver, Jeffrey Hooke, Michael Liebman, "Caffeine Intake, Race, and Risk of Invasive Breast Cancer Lessons Learned from Data Mining a Clinical Database," cbms, pp.714-718, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006
Usage of this product signifies your acceptance of the Terms of Use.