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

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Fan:2015:CEC,

• 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 cross-validation.",
• notes = "See also http://dx.doi.org/10.1155/2015/354807 1150 hrs 15654 CEC2015",

}

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

Citations