Automatic detection of vaccine adverse reactions by incorporating historical medical conditions

Zhonghua Jiang, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper extends the problem of vaccine adverse reaction detection by incorporating historical medical conditions. We propose a novel measure called dual-lift for this task, and formulate this problem in the framework of constraint pattern mining. We present a pattern mining algorithm DLiftMiner which utilizes a novel approach to upper bound the dual-lift measure for reducing the search space. Experimental results on both synthetic and real world datasets show that our method is effective and promising.

Original languageEnglish (US)
Title of host publication2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011
Pages547-549
Number of pages3
DOIs
StatePublished - 2011
Event2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011 - Chicago, IL, United States
Duration: Aug 1 2011Aug 3 2011

Publication series

Name2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011

Other

Other2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011
CountryUnited States
CityChicago, IL
Period8/1/118/3/11

Keywords

  • Adverse reaction
  • Constraint pattern mining
  • Pruning strategy
  • Tough constraint
  • Vaccine

Fingerprint Dive into the research topics of 'Automatic detection of vaccine adverse reactions by incorporating historical medical conditions'. Together they form a unique fingerprint.

Cite this