A genetic algorithm for learning significant phrase patterns in radiology reports

Robert M. Patton; Thomas E. Potok; Barbara G. Beckerman; Jim N. Treadwell

doi:10.1145/1570256.1570380

A genetic algorithm for learning significant phrase patterns in radiology reports

Robert M. Patton, Thomas E. Potok, Barbara G. Beckerman, Jim N. Treadwell

Oak Ridge National Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Publisher	Association for Computing Machinery
Pages	2665-2670
Number of pages	6
ISBN (Print)	9781605583259
DOIs	https://doi.org/10.1145/1570256.1570380
State	Published - 2009
Event	11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 - Montreal, QC, Canada Duration: Jul 8 2009 → Jul 12 2009

Publication series

Name	Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Volume	2009-January

Conference

Conference	11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Country/Territory	Canada
City	Montreal, QC
Period	07/8/09 → 07/12/09

Bibliographical note

Publisher Copyright:
© 2009 ACM.

Keywords

genetic algorithm
information retrieval
learning agents
mammography reports
maximum variation sampling
multi-agent system

Access to Document

10.1145/1570256.1570380

Cite this

Patton, R. M., Potok, T. E., Beckerman, B. G., & Treadwell, J. N. (2009). A genetic algorithm for learning significant phrase patterns in radiology reports. In Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 (pp. 2665-2670). (Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009; Vol. 2009-January). Association for Computing Machinery. https://doi.org/10.1145/1570256.1570380

Patton, Robert M. ; Potok, Thomas E. ; Beckerman, Barbara G. et al. / A genetic algorithm for learning significant phrase patterns in radiology reports. Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009. Association for Computing Machinery, 2009. pp. 2665-2670 (Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009).

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title = "A genetic algorithm for learning significant phrase patterns in radiology reports",

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.",

keywords = "genetic algorithm, information retrieval, learning agents, mammography reports, maximum variation sampling, multi-agent system",

author = "Patton, {Robert M.} and Potok, {Thomas E.} and Beckerman, {Barbara G.} and Treadwell, {Jim N.}",

note = "Publisher Copyright: {\textcopyright} 2009 ACM.; 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 ; Conference date: 08-07-2009 Through 12-07-2009",

year = "2009",

doi = "10.1145/1570256.1570380",

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isbn = "9781605583259",

series = "Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009",

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Patton, RM , Potok, TE, Beckerman, BG & Treadwell, JN 2009, A genetic algorithm for learning significant phrase patterns in radiology reports. in Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009. Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009, vol. 2009-January, Association for Computing Machinery, pp. 2665-2670, 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009, Montreal, QC, Canada, 07/8/09. https://doi.org/10.1145/1570256.1570380

A genetic algorithm for learning significant phrase patterns in radiology reports. / Patton, Robert M.; Potok, Thomas E.; Beckerman, Barbara G. et al.
Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009. Association for Computing Machinery, 2009. p. 2665-2670 (Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009; Vol. 2009-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Patton RM , Potok TE, Beckerman BG, Treadwell JN. A genetic algorithm for learning significant phrase patterns in radiology reports. In Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009. Association for Computing Machinery. 2009. p. 2665-2670. (Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009). doi: 10.1145/1570256.1570380

A genetic algorithm for learning significant phrase patterns in radiology reports

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Keywords

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