Graduation Year
2006
Document Type
Thesis
Degree
M.S.Cp.E.
Degree Granting Department
Computer Science and Engineering
Major Professor
Dmitry Goldgof, Ph.D.
Committee Member
Lawrence Hall, Ph.D.
Committee Member
Sudeep Sarkar, Ph.D.
Keywords
data mining, rule association, medical expert system, apriori, medical implications
Abstract
The availability of new treatments for a disease depends on the success of clinical trials. In order for a clinical trial to be successful and approved, medical researchers must first recruit patients with a specific set of conditions in order to test the effectiveness of the proposed treatment. In the past, the accrual process was tedious and time-consuming. Since accruals rely heavily on the ability of physicians and their staff to be familiar with the protocol eligibility criteria, candidates tend to be missed. This can result and has resulted in unsuccessful trials.A recent project at the University of South Florida aimed to assist research physicians at H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, with a screening process by utilizing a web-based expert system, Moffitt Expedited Accrual Network System (MEANS). This system allows physicians to determine the eligibility of a patient for several clinical trials simultaneously.We have implemented this web-based expert system at the H. Lee Moffitt Cancer Center & Research Gastroenterology (GI) Clinic. Based on our findings and staff feedback, the system has undergone many optimizations. We used data mining techniques to analyze the medical data of current gastrointestinal patients. The use of the Apriori algorithm allowed us to discover new rules (implications) in the patient data. All of the discovered implications were checked for medical validity by a physician, and those that were determined to be valid were entered into the expert system. Additional analysis of the data allowed us to streamline the system and decrease the number of mouse clicks required for screening. We also used a probability-based method to reorder the questions, which decreased the amount of data entry required to determine a patient's ineligibility.
Scholar Commons Citation
Ivanovskiy, Tim V., "Mining Medical Data in a Clinical Environment" (2006). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/3908
