Oral

Data mining, which is defined as the extraction of hidden predictive information from large databases, is a powerful technology with great potential to help public health. This session will focus on the application of data mining and public health to effectively lead to better problem discovery and solution development. Presentations will examine the utilization of electronic health records and population health, and how patterns and associations within the data contained within those records provide opportunities to effectively survey and manage the public health of various communities. On presentation will focus on the techniques of data mining, such as data analysis, preference modeling, simulation and visualization will also be explored and its relationship to the identification of preventable hospital admissions and the subsequent reduction of costs. By identifying significant patterns within the data that lead to a greater understanding of public health issues, such as chronic disease management, facilitate better patient-centered communication by focusing on the core issues relevant to that individual’s health. One such presentation will identify how using this type of decision-tree classification provides a better mechanism for this type of patient-centered communication.

Session Objectives: 1. Describe the tools and techinques of data mining and its relationship to public health 2. Demonstrate how the use of data mining can effectively assit in public health management 3. Explain how the use of decision-tree classification in data mining faciltites patient-centered communication

CE Credits: Medical (CME), Health Education (CHES), Nursing (CNE), Public Health (CPH)