TY - GEN
T1 - Caffeine intake, race, and risk of invasive breast cancer lessons learned from data mining a clinical database
AU - Maskery, Susan
AU - Yonghong, Zhang
AU - Hai, Hu
AU - Shrivel, Craig
AU - Hooke, Jeffrey
AU - Liebman, Michael
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33845591397&partnerID=8YFLogxK
U2 - 10.1109/CBMS.2006.64
DO - 10.1109/CBMS.2006.64
M3 - Conference contribution
AN - SCOPUS:33845591397
SN - 0769525172
SN - 9780769525174
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 714
EP - 718
BT - Proceedings - 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006
T2 - 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006
Y2 - 22 June 2006 through 23 June 2006
ER -