Research Interests
I am interested in using computational techniques to improve the early detection of breast cancer. My work centers on the development of an expert system that can accurately assess the probability of breast cancer using patients’ demographic risk factors and mammography findings. I and colleagues have developed a Bayesian Network that is designed to assist radiologists in the post-discovery aspects of mammography: interpretation and decision-making. Currently, we are looking at whether inductive logic programming (ILP) and statistical relational learning (SRL) can improve the performance of this system.
Selected Publications
Burnside ES, Rubin DL, Shachter R, Sohlich RE, Sickles, EA. A probabilistic expert system that provides automated mammographic-histologic correlation: Initial experience AJR 2004;182(2):481-8.
Rubin DL, Burnside ES, and Shachter R: "A Bayesian network to assist mammography interpretation" in: Sainfort, F., Brandeau, M.L., and W.P. Pierskalla, Eds., Handbook of Operations Research and Health Care: Methods and Applications, Kluwer Academic Publishers, in press.
Burnside ES, Rubin DL, Shachter RD. Using a Bayesian Network to Predict the Probability and Type of Breast Cancer Represented by Microcalcifications on Mammography” in: Fieschi, M., Coiera, E., and Li, Y.J., Eds., Medinfo 2004, Proceedings of the 11th World Congress on Medical Informatics, Sept. 7-11, 2004, IOS Press, 13-18.
Burnside ES, Rubin DL, Shachter RD. Improving a Bayesian Network’s Ability to Predict the Probability of Malignancy of Microcalcifications on Mammography. Proc Computer Assisted Radiology and Surgery 2004, International Congress Series; 1268: 1021-1026.
Pan Y, Burnside ES. The effects of training parameters on learning a probabilistic expert system for mammography. Proc Computer Assisted Radiology and Surgery 2004, International Congress Series; 1268: 1027-1032.
Burnside ES, Park JM. The Use of Batch Reading to Improve the Performance of Screening Mammography. AJR (In Press).
Burnside ES. Computer-Aided Decision Support in Radiology Focusing on Bayesian Networks. Academic Radiology (Submitted)
Burnside ES, Kahn CE. A Tutorial on Artificial Intelligence Techniques Used for Computer-Aided Decision Support in Radiology. Diagn Imaging 2004; November:126-131.
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