Prediction of Acute Hypotensive Episodes Using Random Forest Based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Zhun Fan and Youxiang Zuo and Dazhi Jiang and 
                 Xinye Cai",
  title =        "Prediction of Acute Hypotensive Episodes Using Random
                 Forest Based on Genetic Programming",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "688--694",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7256957",
  abstract =     "At Intensive Care Unit (ICU), acute hypotensive
                 episode (AHE) can cause serious consequences. It can
                 make the organs broken, or even the patient dead.
                 Generally AHE is predicted by the doctor clinically. In
                 order to forecast the AHE automatically, this paper
                 proposes an algorithm based on the genetic programming
                 (GP) and random forest (RF). The algorithm obtains
                 features of the signal through the Intrinsic Mode
                 Function (IMF) signal produced by applying empirical
                 mode decomposition (EMD) to the arterial blood pressure
                 (MAP) signal. Then the feature sets and the data sets
                 are grouped to evolve decision functions via GP.
                 Finally, a random forest is formed and the
                 classification result is obtained by voting. The
                 achieved accuracy of the proposed method is
                 77.55percent, the sensitivity is 80.55percent and
                 specificity is 75.14percent after the five-fold
  notes =        "See also 1150
                 hrs 15654 CEC2015",

Genetic Programming entries for Zhun Fan Youxiang Zuo Dazhi Jiang Xinye Cai