Using Knowledge Discovery in Database & Data Mining to Develop Techniques in Medical Informatics Applied to Surgical Databases

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The tools used in the field of medical informatics are those of knowledge discovery in databases (KDD) or alternatively, data mining. KDD spans a variety of fields: statistics, pattern recognition, artificial intelligence, and data warehousing. However, it has become a distinct discipline with its own objectives. The purpose of data mining is to extract previously unknown and potentially useful information. KDD differs from the more traditional statistical tools by focusing on model identification while minimizing misclassification. Statistical methods tend to focus on estimation and model identification. The hypotheses generated by the KDD process must then be validated by standard statistical techniques. It has become increasingly clear that more traditional techniques are not adequate because of the size and complexity of the databases. One 450-bed hospital maintained a database with six months of laboratory tests and medications as well as summaries of patient followup for 15 months. The storage capacity of the computer had to be increased by 12 gigabytes. The dependence upon an investigator examining relationships in the data must yield to an automated process. KDD can generate interesting and unexpected hypotheses which can be examined by statistical methods. The automatic hypothesis formation and testing cycle can continue until important patterns emerge. This project involves the study and development of data mining techniques applied to several databases primarily stored in the Alliant Health System. This includes a database containing a 25-year followup on a cohort of 10,000 women in Kentucky to examine patterns of health and lifestyle. In addition, the techniques will be applied to a database of student information to examine relationships between student habits, student success, and student learning. Using the developed materials and databases, a course will be taught to students in the Department of Mathematics on applications of data mining. This GOALI project is jointly supported by the MPS Office of Multidisciplinary Activities (OMA) and the Division of Mathematical Sciences (DMS).