DNP 805 Topic 5 Discussion 2

DNP 805 Topic 5 Discussion 2 Using the clinical question you identified from above, determine the individual components to that question and pinpoint the location in the hypothetical database where the information you require will be extracted. When exploring the causes of breast cancer, researchers circle back to genetics. There is well-documented research on women who have mutations in the BRCA1 and BRCA2 genes and eventually develop breast cancer. However, there is new insight into how smaller variations signpost regions in the DNA loci where the genes that affect breast cancer risk are most likely to be located (Michalilidou et al., 2017). While these smaller variations contribute to minimal risk for breast cancer, it is still useful information to have when building the patient profile and allow researchers to develop more targeted screening strategies. The clinical question posed in DQ 1 was: what effects do common gene variations have on breast cancer risk? This question will guide the extraction of the information needed from the hypothetical database. Because genetic variants are part of the clinical data set, the information to explore this question will most likely come from the electronic health record (EHR). For current patients who were diagnosed with breast cancer, they will have had studies done to identify the cause which will include genetic profiling. As this information is extracted from the EHR, the results will need to be cross-referenced with information from a complement database which stores information related to breast cancer datasets including SEER; Wisconsin Diagnosis Breast Cancer; Digital Breast Cancer Surveillance Consortium; and National Cancer Institute’s Surveillance, Epidemiology, and End Results (Oskouei et al., 2017). Utilizing the AR datamining technique, the precise algorithm developed would pull the genetic variant data from the patient records and hunt for associations in the complementary databases for the known genetic variants that are associated with the genes that cause breast cancer. This algorithm could also find correlations and associations with other genetic variants that remain unknown but would add to the existing body of knowledge. Michailidou, K., Linstrom, S., Dennis, J., Beesley, J., Hui, S., Kar, S., Lemacon, A., Soucy, P., Glubb, D., Rostamianfar, A., Bolla, M.K., Wang, Q., Tyler, J., Dicks, E., Lee, A., Wang, Z., Allen, J., Keeman, R.,…Easton, D.F. (2017). Association analysis identifies 65 new breast cancer risk loci. Nature, 551, 92-94. doi: 10.1038/nature24284 Oskouei, R.J., Kor, N.M., & Maleki, S.A. (2017). Data mining and medical world: Breast cancers’ diagnosis, treatment, prognosis and challenges. American Journal of Cancer Research, 7(3), 610-627. Retrieved from EBSCOhost.