Contrast Mining for Pattern Discovery and Descriptive Analytics to Tailor Sub-Groups of Patients Using Big Data Solutions.

The shift to electronic health records has created a plethora of information ready to be examined and acted upon by those in the medical and computational fields. While this allows for novel research on a scale unthinkable in the past, all discoveries still rely on some initial insight leading to a hypothesis. As the size and variety of data grows so do the number of potential findings, making it necessary to optimize hypothesis generation to increase the rate and importance of discoveries produced from the data.

Identifying key variables in African American adherence to colorectal cancer screening: the application of data mining.

Background: This paper reports on an effort to identify a streamlined set of issues important for colorectal cancer communication and interventions with older African Americans.

Methods: African American (N = 1,021), 683 women and 338 men, 50 to 75 years completed a telephone survey addressing demographics, colorectal cancer screening, cancer attitudes, and cancer related cultural attitudes. Several data analytics methods were applied and evaluated.