Cancer epidemiology of small communities: Using a novel approach to detecting clusters

E. Kordysh; A. Bolotin; M. Barchana; R. Chen

doi:10.1007/3-540-45497-7_19

Cancer epidemiology of small communities: Using a novel approach to detecting clusters

E. Kordysh
, A. Bolotin
, M. Barchana
, R. Chen

Ben-Gurion University of the Negev
Israel National Cancer Registry
Impulse Dynamics

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

Abstract

Cancer cluster detection in small communities is an important but complicated field of cancer epidemiology, due to large statistical errors of both types associated with the detection. In this paper, authors show the use of a new approach to this problem. This approach is based on three complementary techniques. One is aimed at detection of the cluster, and two others are applied after cluster detection in order to confirm or reject the cluster. Included is application of the approach in small agricultural-industrial communities of the South of Israel. The approach reduces both types of statistical errors, increases the chance to detect a true clustering and enables a first step in the identification of the cause of a cluster detected.

Original language	English
Title of host publication	Medical Data Analysis - 2nd International Symposium, ISMDA 2001, Proceedings
Editors	Jose Crespo, Victor Maojo, Fernando Martin
Publisher	Springer Verlag
Pages	126-132
Number of pages	7
ISBN (Electronic)	3540427341, 9783540427346
DOIs	https://doi.org/10.1007/3-540-45497-7_19
State	Published - 2001
Externally published	Yes
Event	2nd International Symposium on Medical Data Analysis, ISMDA 2001 - Madrid, Spain Duration: 8 Oct 2001 → 9 Oct 2001

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2199
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Symposium on Medical Data Analysis, ISMDA 2001
Country/Territory	Spain
City	Madrid
Period	8/10/01 → 9/10/01

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Cancer epidemiology
Cluster analysis for medical applications
Small agricultural-industrial communities
Temporal pattern analysis

Access to Document

10.1007/3-540-45497-7_19

Cite this

Kordysh, E., Bolotin, A., Barchana, M., & Chen, R. (2001). Cancer epidemiology of small communities: Using a novel approach to detecting clusters. In J. Crespo, V. Maojo, & F. Martin (Eds.), Medical Data Analysis - 2nd International Symposium, ISMDA 2001, Proceedings (pp. 126-132). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2199). Springer Verlag. https://doi.org/10.1007/3-540-45497-7_19

Kordysh, E. ; Bolotin, A. ; Barchana, M. et al. / Cancer epidemiology of small communities : Using a novel approach to detecting clusters. Medical Data Analysis - 2nd International Symposium, ISMDA 2001, Proceedings. editor / Jose Crespo ; Victor Maojo ; Fernando Martin. Springer Verlag, 2001. pp. 126-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Cancer cluster detection in small communities is an important but complicated field of cancer epidemiology, due to large statistical errors of both types associated with the detection. In this paper, authors show the use of a new approach to this problem. This approach is based on three complementary techniques. One is aimed at detection of the cluster, and two others are applied after cluster detection in order to confirm or reject the cluster. Included is application of the approach in small agricultural-industrial communities of the South of Israel. The approach reduces both types of statistical errors, increases the chance to detect a true clustering and enables a first step in the identification of the cause of a cluster detected.",

keywords = "Cancer epidemiology, Cluster analysis for medical applications, Small agricultural-industrial communities, Temporal pattern analysis",

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Kordysh, E, Bolotin, A, Barchana, M & Chen, R 2001, Cancer epidemiology of small communities: Using a novel approach to detecting clusters. in J Crespo, V Maojo & F Martin (eds), Medical Data Analysis - 2nd International Symposium, ISMDA 2001, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2199, Springer Verlag, pp. 126-132, 2nd International Symposium on Medical Data Analysis, ISMDA 2001, Madrid, Spain, 8/10/01. https://doi.org/10.1007/3-540-45497-7_19

Cancer epidemiology of small communities: Using a novel approach to detecting clusters. / Kordysh, E.; Bolotin, A.; Barchana, M. et al.
Medical Data Analysis - 2nd International Symposium, ISMDA 2001, Proceedings. ed. / Jose Crespo; Victor Maojo; Fernando Martin. Springer Verlag, 2001. p. 126-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2199).

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

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N2 - Cancer cluster detection in small communities is an important but complicated field of cancer epidemiology, due to large statistical errors of both types associated with the detection. In this paper, authors show the use of a new approach to this problem. This approach is based on three complementary techniques. One is aimed at detection of the cluster, and two others are applied after cluster detection in order to confirm or reject the cluster. Included is application of the approach in small agricultural-industrial communities of the South of Israel. The approach reduces both types of statistical errors, increases the chance to detect a true clustering and enables a first step in the identification of the cause of a cluster detected.

AB - Cancer cluster detection in small communities is an important but complicated field of cancer epidemiology, due to large statistical errors of both types associated with the detection. In this paper, authors show the use of a new approach to this problem. This approach is based on three complementary techniques. One is aimed at detection of the cluster, and two others are applied after cluster detection in order to confirm or reject the cluster. Included is application of the approach in small agricultural-industrial communities of the South of Israel. The approach reduces both types of statistical errors, increases the chance to detect a true clustering and enables a first step in the identification of the cause of a cluster detected.

KW - Cancer epidemiology

KW - Cluster analysis for medical applications

KW - Small agricultural-industrial communities

KW - Temporal pattern analysis

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Kordysh E, Bolotin A, Barchana M, Chen R. Cancer epidemiology of small communities: Using a novel approach to detecting clusters. In Crespo J, Maojo V, Martin F, editors, Medical Data Analysis - 2nd International Symposium, ISMDA 2001, Proceedings. Springer Verlag. 2001. p. 126-132. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45497-7_19

Cancer epidemiology of small communities: Using a novel approach to detecting clusters

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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Cite this