Robert M. Patton
ABSTRACT
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.
- Abdalla, R.M., and Teufel, S. 2006. A bootstrapping approach to unsupervised detection of cue phrase variants. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics (Sydney, Australia). COLING 2006. ACM Press, New York, NY, 2061--2064. Google Scholar
Digital Library
- Cheng, W., Greaves, C. and Warren, M. 2006. From n-gram to skipgram to concgram. International Journal of Corpus Linguistics 11/4: 411--33.Google ScholarCross Ref
- Dridi, O.; Ben Ahmed, M., "Building an Ontology-Based Framework For Semantic Information Retrieval: Application To Breast Cancer," Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd International Conference on, pp.1--6, 7-11 April 2008.Google Scholar
- Duh, K., and Kirchhoff, K. 2004. Automatic learning of language model structure. In Proceedings of the 20th International Conference on Computational Linguistics (Geneva, Switzerland). COLING 2004. ACM Press, New York, NY, 2061--2064. Google ScholarDigital Library
- Fox, C. 1992. "Lexical analysis and stoplists." In Information Retrieval: Data Structures and Algorithms (ed. W.B. Frakes and R. Baeza-Yates), Englewood Cliffs, NJ: Prentice Hall. Google ScholarDigital Library
- Jing-Yan Wang; Zhen Zhu, "Framework of multi-agent information retrieval system based on ontology and its application," Machine Learning and Cybernetics, 2008 International Conference on, pp.1615--1620, 12-15 July 2008.Google Scholar
- Kai Kang; Kunhui Lin; Changle Zhou; Feng Guo, "Domain-Specific Information Retrieval Based on Improved Language Model," Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on, pp.374--378, 24-27 Aug. 2007. Google ScholarDigital Library
- Patton, M.Q. 1990. Qualitative Evaluation and Research Methods, Second Edition. Newbury Park, CA: Sage Publications, Inc.Google Scholar
- Patton, R.M., Beckerman, B., and Potok, T.E. 2008. Analysis of mammography reports using maximum variation sampling. Proceedings of the 4th GECCO Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC), Atlanta, USA, July 2008. ACM Press, New York, NY, 2061--2064. Google ScholarDigital Library
- Pirkola, A, Keskustalo, H., Leppänen, E., Känsälä, A.and Järvelin, K. 2002. "Targeted s-gram matching: a novel n-gram matching technique for cross- and monolingual word form variants." Information Research, 7(2) {Available at http://InformationR.net/ir/7-2/paper126.html}Google Scholar
- Porter, M. 1980. "An algorithm for suffix stripping." Program vol. 14, pp. 130--137.Google ScholarCross Ref
- Porter Stemming Algorithm. Current Feb. 5, 2009. http://www.tartarus.org/~martin/PorterStemmer/Google Scholar
- Raghavan, V.V., and Wong, S.K.M. 1986. "A critical analysis of vector space model for information retrieval." Journal of the American Society for Information Science, Vol.37 (5), p. 279--87.Google ScholarCross Ref
- Reed, J.W., Potok, T.E., and Patton, R.M. 2004. "A multi-agent system for distributed cluster analysis," in Proceedings of Third International Workshop on Software Engineering for Large-Scale Multi-Agent Systems (SELMAS'04) Workshop in conjunction with the 26th International Conference on Software Engineering Edinburgh, Scotland, UK: IEE, pp. 152--5. Google ScholarDigital Library
- Rudolph, G., "Convergence analysis of canonical genetic algorithms," Neural Networks, IEEE Transactions on, vol.5, no.1, pp.96--101, Jan 1994.Google ScholarDigital Library
- Salton, G. 1983. Introduction to Modern Information Retrieval. McGraw-Hill. Google ScholarDigital Library
- Siddiqui, T.J., "Integrating notion of agency and semantics in information retrieval: an intelligent multi-agent model," Intelligent Systems Design and Applications, 2005. ISDA '05. Proceedings. 5th International Conference on, pp. 160--165, 8-10 Sept. 2005. Google ScholarDigital Library
Index Terms
- A genetic algorithm for learning significant phrase patterns in radiology reports
Recommendations
An improved genetic algorithm with conditional genetic operators and its application to set-covering problem
The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an ...
Hybrid Taguchi-genetic algorithm for global numerical optimization
In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is proposed to solve global numerical optimization problems with continuous variables. The HTGA combines the traditional genetic algorithm (TGA), which has a powerful global exploration capability,...
Solving Japanese nonograms by Taguchi-based genetic algorithm
A Taguchi-based genetic algorithm (TBGA) is proposed to solve Japanese nonogram puzzles. The TBGA exploits the power of global exploration inherent in the traditional genetic algorithm (GA) and the abilities of the Taguchi method in efficiently ...
Comments