Mining Bayesian Networks to Forecast Adverse Outcomes Related to Acute Coronary Syndrome

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Andrew J. Novobilski and Francis M. Fesmire and 
                 David Sonnemaker",
  title =        "Mining {Bayesian} Networks to Forecast Adverse
                 Outcomes Related to Acute Coronary Syndrome",
  booktitle =    "Proceedings of the Seventeenth International Florida
                 Artificial Intelligence Research Society Conference",
  year =         "2004",
  editor =       "Valerie Barr and Zdravko Markov",
  address =      "Miami Beach, Florida, USA",
  month =        may # " 17-19",
  organisation = "In cooperation with The American Association for
                 Artificial Intelligence",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming, Bayesian
                 Networks, datamining",
  ISBN =         "1-57735-201-7",
  URL =          "",
  size =         "6 pages",
  abstract =     "One fascinating aspect of tool building for datamining
                 is the application of a generalised datamining tool to
                 a specific domain. Often times, this process results in
                 a cross disciplinary analysis of both the datamining
                 technique and the application of the results to the
                 domain itself. This process of cross-disciplinary
                 analysis often leads not only to improvements of the
                 tool, but more importantly, to a better understanding
                 of the underlying domain model for the domain experts
                 involved. This paper presents the results of applying a
                 datamining tool for identifying a Bayesian Network to
                 represent a dataset of triage information taken from
                 patients arriving at the emergency room with symptoms
                 of Acute Coronary Syndrome. Specifically, a domain
                 expert generated Bayesian Network and a mined Bayesian
                 Network, both trained using the triage dataset, are
                 compared for their accuracy in forecasting 30-day
                 adverse outcomes for the patients represented in the
                 dataset. The comparison, done using ROC curves, shows
                 that the mined Bayesian Networked slightly outperformed
                 the domain expert generated network. The results are
                 discussed and direction for future work based on the
                 complexity of the mined network versus the expert's
                 network are presented..",
  bibsource =    "DBLP,",
  notes =        "no explicit mention of GP

Genetic Programming entries for Andrew J Novobilski Francis M Fesmire David Sonnemaker