Discovering potential precursors of mammography abnormalities based on textual features, frequencies, and sequences

Robert M. Patton; Thomas E. Potok

doi:10.1007/978-3-642-13208-7_82

Discovering potential precursors of mammography abnormalities based on textual features, frequencies, and sequences

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

Abstract

Diagnosing breast cancer from mammography reports is heavily dependant on the time sequences of the patient visits. In the work described, we take a longitudinal view of the text of a patient's mammogram reports to explore the existence of certain phrase patterns that indicate future abnormalities may exist for the patient. Our approach uses various text analysis techniques combined with Haar wavelets for the discovery and analysis of such precursor phrase patterns. We believe the results show significant promise for the early detection of breast cancer and other breast abnormalities.

Original language	English
Title of host publication	Artificial Intelligence and Soft Computing - 10th International Conference, ICAISC 2010
Pages	657-664
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-13208-7_82
State	Published - 2010
Event	10th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2010 - Zakopane, Poland Duration: Jun 13 2010 → Jun 17 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6113 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2010
Country/Territory	Poland
City	Zakopane
Period	06/13/10 → 06/17/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-642-13208-7_82

Cite this

Patton, R. M., & Potok, T. E. (2010). Discovering potential precursors of mammography abnormalities based on textual features, frequencies, and sequences. In Artificial Intelligence and Soft Computing - 10th International Conference, ICAISC 2010 (PART 1 ed., pp. 657-664). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6113 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-13208-7_82

Patton, Robert M. ; Potok, Thomas E. / Discovering potential precursors of mammography abnormalities based on textual features, frequencies, and sequences. Artificial Intelligence and Soft Computing - 10th International Conference, ICAISC 2010. PART 1. ed. 2010. pp. 657-664 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Discovering potential precursors of mammography abnormalities based on textual features, frequencies, and sequences",

abstract = "Diagnosing breast cancer from mammography reports is heavily dependant on the time sequences of the patient visits. In the work described, we take a longitudinal view of the text of a patient's mammogram reports to explore the existence of certain phrase patterns that indicate future abnormalities may exist for the patient. Our approach uses various text analysis techniques combined with Haar wavelets for the discovery and analysis of such precursor phrase patterns. We believe the results show significant promise for the early detection of breast cancer and other breast abnormalities.",

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Patton, RM & Potok, TE 2010, Discovering potential precursors of mammography abnormalities based on textual features, frequencies, and sequences. in Artificial Intelligence and Soft Computing - 10th International Conference, ICAISC 2010. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6113 LNAI, pp. 657-664, 10th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2010, Zakopane, Poland, 06/13/10. https://doi.org/10.1007/978-3-642-13208-7_82

Discovering potential precursors of mammography abnormalities based on textual features, frequencies, and sequences. / Patton, Robert M.; Potok, Thomas E.
Artificial Intelligence and Soft Computing - 10th International Conference, ICAISC 2010. PART 1. ed. 2010. p. 657-664 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6113 LNAI, No. PART 1).

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

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N2 - Diagnosing breast cancer from mammography reports is heavily dependant on the time sequences of the patient visits. In the work described, we take a longitudinal view of the text of a patient's mammogram reports to explore the existence of certain phrase patterns that indicate future abnormalities may exist for the patient. Our approach uses various text analysis techniques combined with Haar wavelets for the discovery and analysis of such precursor phrase patterns. We believe the results show significant promise for the early detection of breast cancer and other breast abnormalities.

AB - Diagnosing breast cancer from mammography reports is heavily dependant on the time sequences of the patient visits. In the work described, we take a longitudinal view of the text of a patient's mammogram reports to explore the existence of certain phrase patterns that indicate future abnormalities may exist for the patient. Our approach uses various text analysis techniques combined with Haar wavelets for the discovery and analysis of such precursor phrase patterns. We believe the results show significant promise for the early detection of breast cancer and other breast abnormalities.

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Patton RM , Potok TE. Discovering potential precursors of mammography abnormalities based on textual features, frequencies, and sequences. In Artificial Intelligence and Soft Computing - 10th International Conference, ICAISC 2010. PART 1 ed. 2010. p. 657-664. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-13208-7_82

Discovering potential precursors of mammography abnormalities based on textual features, frequencies, and sequences

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