Detection of Acute Hypotensive Episodes via Empirical Mode Decomposition and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Jiang:2014:IIKI,
  author =       "Dazhi Jiang and Liyu Li and Zhun Fan and Jin Liu",
  booktitle =    "2014 International Conference on Identification,
                 Information and Knowledge in the Internet of Things
                 (IIKI)",
  title =        "Detection of Acute Hypotensive Episodes via Empirical
                 Mode Decomposition and Genetic Programming",
  year =         "2014",
  pages =        "225--228",
  abstract =     "Big data time series in the Intensive Care Unit (ICU)
                 is now touted as a solution to help clinicians to
                 diagnose the case of the physiological disorder and
                 select proper treatment based on this diagnosis. Acute
                 Hypotensive Episodes (AHE) is one of the hemodynamic
                 instabilities with high mortality rate that is frequent
                 among many groups of patients. This study presented a
                 methodology to predict AHE for ICU patients based on
                 big data time series. Empirical Mode Decomposition
                 (EMD) was used to calculate patient's Mean Arterial
                 Pressure (MAP) time series and some features, which are
                 bandwidth of the amplitude modulation, frequency
                 modulation and power of Intrinsic Mode Function (IMF)
                 were extracted. Then, the Genetic Programming (GP) is
                 used to build the classification model for detection of
                 AHE. The methodology was applied in the datasets of the
                 10th Physio Net and Computers Cardiology Challenge in
                 2009 and Multi-parameter Intelligent Monitoring for
                 Intensive Care (MIMIC-II). We achieve the accuracy of
                 83.33percent in the training set and 91.89percent in
                 the testing set of the 2009 challenge's dataset, and
                 the 83.37percent in the training set and 80.64percent
                 in the testing set of the MIMIC-II dataset.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/IIKI.2014.53",
  month =        oct,
  notes =        "Also known as \cite{7064034}",
}

Genetic Programming entries for Dazhi Jiang Liyu Li Zhun Fan Jin Liu

Citations