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Mining risk patterns in medical data

Published: 21 August 2005 Publication History

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.

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cover image ACM Conferences
KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
August 2005
844 pages
ISBN:159593135X
DOI:10.1145/1081870
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: 21 August 2005

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Author Tags

  1. medical application
  2. optimal risk pattern set
  3. relative risk
  4. rule

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  • (2022)Using the Diagnostic Odds Ratio to Select Patterns to Build an Interpretable Pattern-Based Classifier in a Clinical Domain: Multivariate Sequential Pattern Mining StudyJMIR Medical Informatics10.2196/3231910:8(e32319)Online publication date: 10-Aug-2022
  • (2022)Commonality Analysis for Detecting Failures Caused by Inspection Tools in Semiconductor Manufacturing ProcessesIEEE Transactions on Semiconductor Manufacturing10.1109/TSM.2022.320165435:4(596-604)Online publication date: Nov-2022
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