A genetic algorithm for learning significant phrase patterns in radiology reports

Radiologists disagree with each other over the characteristics and features of what constitutes a normal mammogram and the terminology to use in the associated radiology report. Recently, the focus has been on classifying abnormal or suspicious reports, but even this process needs further layers of clustering and gradation, so that individual lesions can be more effectively classified. Using a genetic algorithm, the approach described here successfully learns phrase patterns for two distinct classes of radiology reports (normal and abnormal). These patterns can then be used as a basis for automatically analyzing, categorizing, clustering, or retrieving relevant radiology reports for the user. Categories and Subject Descriptors I.2.6 [Learning]: analogies, concept learning, connectionism and neural nets, induction, knowledge acquisition, language acquisition, parameter learning. H.3.3 [Information Search and Retrieval]: clustering, information filtering, query formulation, relevance fe...

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Robert M. Patton, Thomas E. Potok, Barbara G. Beckerman, Jim N. Treadwell
• Keywords: Associated Radiology Report, GECCO 2009, Genetic Algorithm, Optimization, Radiology Reports