Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining.

Journal: Artificial intelligence in medicine
Published Date: Nov 25, 2016

Abstract

OBJECTIVES: Traditional studies on effects of outdoor pollution on asthma have been criticized for questionable statistical validity and inefficacy in exploring the effects of multiple air pollutants, alone and in combination. Association rule mining (ARM), a method easily interpretable and suitable for the analysis of the effects of multiple exposures, could be of use, but the traditional interest metrics of support and confidence need to be substituted with metrics that focus on risk variations caused by different exposures.

Authors

  • Giulia Toti
    Department of Computer Science, University of Houston, Philip Guthrie Hoffman Hall, 3551 Cullen Blvd., Room 501, Houston, TX 77204-3010, USA. Electronic address: giulia.toti@kcl.ac.uk.
  • Ricardo Vilalta
    Department of Computer Science, University of Houston, Philip Guthrie Hoffman Hall, 3551 Cullen Blvd., Room 501, Houston, TX 77204-3010, USA.
  • Peggy Lindner
    Honors College, University of Houston, M.D Anderson Library, 4333 University Dr, Room 212, Houston, TX 77204-2001, USA.
  • Barry Lefer
    Department of Earth and Atmospheric Sciences, University of Houston, Science & Research Building 1, 3507 Cullen Blvd, Room 312, Houston, TX 77204-5007, USA; Now at: Earth Sciences Division, NASA Headquarters, 300 E St SW, Washington, DC 20546, USA.
  • Charles Macias
    Department of Pediatrics, Baylor College of Medicine/Texas Children's Hospital, One Baylor Plaza, Houston, TX 77030, USA.
  • Daniel Price
    Honors College, University of Houston, M.D Anderson Library, 4333 University Dr, Room 212, Houston, TX 77204-2001, USA.

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