analysis of mammography reports using maximum variation sampling
A genetic algorithm (GA) was developed to implement a maximum variation sampling technique to derive a subset of data from a large dataset of unstructured mammography reports. It is well known that a genetic algorithm performs very well for large search spaces and is easily scalable to the size of the data set. In mammography, much effort has been expended to characterize findings in the radiology reports. Existing computer-assisted technologies for mammography are based on machine-learning algorithms that must learn against a training set with known pathologies in order to further refine the algorithms with higher validity of truth. In a large database of reports and corresponding images, automated tools are needed just to determine which data to include in the training set. This work presents preliminary results showing the use of a GA for finding abnormal reports without a training set. The underlying premise is that abnormal reports should consist of unusual or rare words, thereby making the reports very dissimilar in comparison to other reports. A genetic algorithm was developed to test this hypothesis, and preliminary results show that most abnormal reports in a test set are found and can be adequately differentiated.
maximum variation sampling genetic algorithms unstructured radiology reports text analysis