Lexical analysis and stoplists
Information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Software Engineering for Large-Scale Multi-agent Systems SELMAS'04
Proceedings of the 26th International Conference on Software Engineering
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
A bootstrapping approach to unsupervised detection of cue phrase variants
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automatic learning of language model structure
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Domain-Specific Information Retrieval Based on Improved Language Model
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Analysis of mammography reports using maximum variation sampling
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Architecture-level dependability analysis of a medical decision support system
Proceedings of the 2010 ICSE Workshop on Software Engineering in Health Care
Genetic algorithm for analysis of abdominal aortic aneurysms in radiology reports
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Characterizing mammography reports for health analytics
Proceedings of the 1st ACM International Health Informatics Symposium
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Characterizing Mammography Reports for Health Analytics
Journal of Medical Systems
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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.