Mining risk patterns in medical data

Jiuyong Li; Ada Wai Chee Fu; Hongxing He; Jie Chen; Huidong Jin; Damien McAullay; Graham Williams; Ross Sparks; Chris Kelman

doi:10.1145/1081870.1081971

Mining risk patterns in medical data

Jiuyong Li^*, Ada Wai Chee Fu, Hongxing He, Jie Chen, Huidong Jin, Damien McAullay, Graham Williams, Ross Sparks, Chris Kelman

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

69 Citations (Scopus)

Abstract

In this paper, we discuss a problem of finding risk patterns in medical data, We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.

Original language	English
Pages	770-775
Number of pages	6
DOIs	https://doi.org/10.1145/1081870.1081971
Publication status	Published - 2005
Externally published	Yes
Event	KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Chicago, IL, United States Duration: 21 Aug 2005 → 24 Aug 2005

Conference

Conference	KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country/Territory	United States
City	Chicago, IL
Period	21/08/05 → 24/08/05

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10.1145/1081870.1081971

Cite this

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title = "Mining risk patterns in medical data",

abstract = "In this paper, we discuss a problem of finding risk patterns in medical data, We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.",

keywords = "Medical application, Optimal risk pattern set, Relative risk, Rule",

author = "Jiuyong Li and Fu, \{Ada Wai Chee\} and Hongxing He and Jie Chen and Huidong Jin and Damien McAullay and Graham Williams and Ross Sparks and Chris Kelman",

year = "2005",

doi = "10.1145/1081870.1081971",

language = "English",

pages = "770--775",

note = "KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ; Conference date: 21-08-2005 Through 24-08-2005",

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TY - CONF

T1 - Mining risk patterns in medical data

AU - Li, Jiuyong

AU - Fu, Ada Wai Chee

AU - He, Hongxing

AU - Chen, Jie

AU - Jin, Huidong

AU - McAullay, Damien

AU - Williams, Graham

AU - Sparks, Ross

AU - Kelman, Chris

PY - 2005

Y1 - 2005

N2 - In this paper, we discuss a problem of finding risk patterns in medical data, We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.

AB - In this paper, we discuss a problem of finding risk patterns in medical data, We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.

KW - Medical application

KW - Optimal risk pattern set

KW - Relative risk

KW - Rule

UR - https://www.scopus.com/pages/publications/32344438056

U2 - 10.1145/1081870.1081971

DO - 10.1145/1081870.1081971

M3 - Paper

AN - SCOPUS:32344438056

SP - 770

EP - 775

T2 - KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Y2 - 21 August 2005 through 24 August 2005

ER -

Mining risk patterns in medical data

Abstract

Conference

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