Medical Outcome Prediction for Intensive Care Unit Patients

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Ludwig:2010:IJCMAM,
  author =       "Simone A. Ludwig and Stefanie Roos and 
                 Monique Frize and Nicole Yu",
  title =        "Medical Outcome Prediction for Intensive Care Unit
                 Patients",
  journal =      "International Journal of Computational Models and
                 Algorithms in Medicine (IJCMAM)",
  year =         "2010",
  volume =       "1",
  number =       "4",
  pages =        "19--30",
  keywords =     "genetic algorithms, genetic programming, Intelligent
                 Technologies",
  ISSN =         "1947-3133",
  URL =          "http://www.irma-international.org/article/medical-outcome-prediction-intensive-care/51668/",
  DOI =          "doi:10.4018/jcmam.2010100102",
  size =         "12",
  abstract =     "The rate of people dying from medical errors in
                 hospitals each year is very high. Errors that
                 frequently occur during the course of providing health
                 care are adverse drug events and improper transfusions,
                 surgical injuries and wrong-site surgery, suicides,
                 restraint-related injuries or death, falls, burns,
                 pressure ulcers, and mistaken patient identities.
                 Medical decision support systems play an increasingly
                 important role in medical practice. By assisting
                 physicians in making clinical decisions, medical
                 decision support systems improve the quality of medical
                 care. Two approaches have been investigated for the
                 prediction of medical outcomes: hours of ventilation
                 and the mortality rate in the adult intensive care
                 unit. The first approach is based on neural networks
                 with the weight-elimination algorithm, and the second
                 is based on genetic programming. Both approaches are
                 compared to commonly used machine learning algorithms.
                 Results show that both algorithms developed score well
                 for the outcomes selected",
  notes =        "Simone A. Ludwig (North Dakota State University, USA),
                 Stefanie Roos (Darmstadt University, Germany), Monique
                 Frize (Carleton University, Canada), and Nicole Yu
                 (Carleton University, Canada)",
}

Genetic Programming entries for Simone A Ludwig Stefanie Roos Monique Frize Nicole J Yu

Citations