technical-note

A genetic algorithm for learning significant phrase patterns in radiology reports

Authors:

Robert M. Patton,

Thomas E. Potok,

Barbara G. Beckerman,

Jim N. TreadwellAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Pages 2665 - 2670

https://doi.org/10.1145/1570256.1570380

Published: 08 July 2009 Publication History

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.

References

[1]

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.

Digital Library

[2]

Cheng, W., Greaves, C. and Warren, M. 2006. From n-gram to skipgram to concgram. International Journal of Corpus Linguistics 11/4: 411--33.

[3]

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.

[4]

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.

Digital Library

[5]

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.

Digital Library

[6]

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.

[7]

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.

Digital Library

[8]

Patton, M.Q. 1990. Qualitative Evaluation and Research Methods, Second Edition. Newbury Park, CA: Sage Publications, Inc.

[9]

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.

Digital Library

[10]

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}

[11]

Porter, M. 1980. "An algorithm for suffix stripping." Program vol. 14, pp. 130--137.

[12]

Porter Stemming Algorithm. Current Feb. 5, 2009. http://www.tartarus.org/~martin/PorterStemmer/

[13]

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.

[14]

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.

Digital Library

[15]

Rudolph, G., "Convergence analysis of canonical genetic algorithms," Neural Networks, IEEE Transactions on, vol.5, no.1, pp.96--101, Jan 1994.

Digital Library

[16]

Salton, G. 1983. Introduction to Modern Information Retrieval. McGraw-Hill.

Digital Library

[17]

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.

Digital Library

Cited By

Patton RRojas CBeckerman BPotok T(2011)A Computational Framework for Search, Discovery, and Trending of Patient Health in Radiology ReportsProceedings of the 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology10.1109/HISB.2011.4(104-111)Online publication date: 26-Jul-2011
https://dl.acm.org/doi/10.1109/HISB.2011.4
Patton RBeaver JPotok T(2011)Classification of Distributed Data Using Topic Modeling and Maximum Variation SamplingProceedings of the 2011 44th Hawaii International Conference on System Sciences10.1109/HICSS.2011.101(1-5)Online publication date: 4-Jan-2011
https://dl.acm.org/doi/10.1109/HICSS.2011.101
Rojas CPatton RBeckerman B(2011)Characterizing Mammography Reports for Health AnalyticsJournal of Medical Systems10.1007/s10916-011-9685-235:5(1197-1210)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1007/s10916-011-9685-2
Show More Cited By

Index Terms

A genetic algorithm for learning significant phrase patterns in radiology reports
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
    2. Learning settings
2. Information systems
  1. Information retrieval

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

July 2009

1760 pages

ISBN:9781605585055

DOI:10.1145/1570256

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 July 2009

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GECCO09

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Patton RRojas CBeckerman BPotok T(2011)A Computational Framework for Search, Discovery, and Trending of Patient Health in Radiology ReportsProceedings of the 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology10.1109/HISB.2011.4(104-111)Online publication date: 26-Jul-2011
https://dl.acm.org/doi/10.1109/HISB.2011.4
Patton RBeaver JPotok T(2011)Classification of Distributed Data Using Topic Modeling and Maximum Variation SamplingProceedings of the 2011 44th Hawaii International Conference on System Sciences10.1109/HICSS.2011.101(1-5)Online publication date: 4-Jan-2011
https://dl.acm.org/doi/10.1109/HICSS.2011.101
Rojas CPatton RBeckerman B(2011)Characterizing Mammography Reports for Health AnalyticsJournal of Medical Systems10.1007/s10916-011-9685-235:5(1197-1210)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1007/s10916-011-9685-2
Patton RPotok T(2010)Discovering potential precursors of mammography abnormalities based on textual features, frequencies, and sequencesProceedings of the 10th international conference on Artificial intelligence and soft computing: Part I10.5555/1894214.1894300(657-664)Online publication date: 13-Jun-2010
https://dl.acm.org/doi/10.5555/1894214.1894300
Rojas CPatton RBeckerman BÇatalyürek ÜLuo GAndrade HSmalheiser N(2010)Characterizing mammography reports for health analyticsProceedings of the 1st ACM International Health Informatics Symposium10.1145/1882992.1883022(201-209)Online publication date: 11-Nov-2010
https://dl.acm.org/doi/10.1145/1882992.1883022
Patton RBeckerman BPotok TTreadwell JPelikan MBranke J(2010)Genetic algorithm for analysis of abdominal aortic aneurysms in radiology reportsProceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830828(1931-1936)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830761.1830828
Pullum LSymons CPatton RBeckerman BClarke LWeber-Jahnke J(2010)Architecture-level dependability analysis of a medical decision support systemProceedings of the 2010 ICSE Workshop on Software Engineering in Health Care10.1145/1809085.1809096(83-88)Online publication date: 3-May-2010
https://dl.acm.org/doi/10.1145/1809085.1809096
Smith S(2010)A Review of Medical Applications of Genetic and Evolutionary ComputationGenetic and Evolutionary Computation10.1002/9780470973134.ch3(17-43)Online publication date: 3-Dec-2010
https://doi.org/10.1002/9780470973134.ch3

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