Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Hybrid Group Search Optimizer with Opposition-Based Learning and Differential Evolution Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Predicting Acute Hypotensive Episodes Based on Multi GP

Authors : Dazhi Jiang, Bo Hu, Zhijian Wu

Published in: Computational Intelligence and Intelligent Systems

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Acute Hypotensive Episodes (AHE) is one of the hemodynamic instabilities with high mortality rate that is common among patients. Timely and rapid intervention is necessary to save patient’s life. This paper presents a methodology to predict AHE for ICU patients based on the Multi Genetic Programming (Multi GP). The methodology is applied to the dataset obtained from Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC-II). The achieved accuracy of the proposed methodology is 79.07 % in the training set and 77.98 % in the testing set with the five-fold cross-validation.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter An Optimized Clustering Algorithm Using Improved Gene Expression Programming
next chapter Research on Evolution Mechanism in Different-Structure Module Redundancy Fault-Tolerant System
Literature
1.
Moody, G.B, Lehman, L.H.: Predicting acute hypotensive episodes: the 10th annual physioNet/computers in cardiology challenge. In: Proceedings of IEEE Computers in Cardiology, pp. 541–544 (2009)
2.
Gang, D., Shi-Sheng, Z., Yang, L.: Time series prediction using wavelet process neural network. Chin. Phys. B 17(6), 1998 (2008)CrossRef
3.
Bassale, J.: Hypotension prediction arterial blood pressure variability. Tecnhical report (2001)
4.
Saeed, M.: Temporal pattern recognition in multiparameter ICU data. Ph.D. thesis, Massachusetts Institute of Technology, USA (2007)
5.
Rocha, T., Paredes, S., De Carvalho, P., et al.: Prediction of acute hypotensive episodes by means of neural network multi-models. Comput. Biol. Med. 41(10), 881–890 (2011)CrossRef
6.
Awandekar, V., Cheeran, A.N.: Predicting acute hypotensive episode by bhattacharyya distance. Int. J. Eng. Res. Appl. 3(2), 370–372 (2013)
7.
Chen, X., Xu, D., Zhang, G., et al.: Forecasting acute hypotensive episodes in intensive care patients based on a peripheral arterial blood pressure waveform. In: Proceedings of IEEE Computers in Cardiology 2009, pp. 545–548 (2009)
8.
Mneimneh, M.A., Povinelli, R.J.: A rule-based approach for the prediction of acute hypotensive episodes. In: Proceedings of IEEE Computers in Cardiology 2009, pp. 557–560 (2009)
9.
Fournier, P.A., Roy, J.F.: Acute hypotension episode prediction using information divergence for feature selection, and non-parametric methods for classification. In: Proceedings of IEEE Computers in Cardiology 2009, pp. 625–628 (2009)
10.
Ho, T.C.T., Chen, X.: Utilizing histogram to identify patients using pressors for acute hypotension. In: Proceedings of IEEE Computers in Cardiology 2009, pp. 797–800 (2009)
11.
Lin, C.S., Chiu, J.S., Hsieh, M.H., et al.: Predicting hypotensive episodes during spinal anesthesia with the application of artificial neural networks. Comput. Methods Programs Biomed. 92(2), 193–197 (2008)CrossRef
12.
Frölich, M.A., Caton, D.: Baseline heart rate may predict hypotension after spinal anesthesia in prehydrated obstetrical patients. Can. J. Anesth. 49(2), 185–189 (2002)CrossRef
13.
Ghaffari, A., Homaeinezhad, M.R., Atarod, M., et al.: Detection of acute hypotensive episodes via a trained adaptive network-based fuzzy inference system (ANFIS). J. Electr. Electron. Eng. Res. 2(2), 025–047 (2010)
14.
Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATH
15.
Gray, H.F., Maxwell, R.J., Martínez-Pérez, I., et al.: Genetic programming for classification and feature selection: analysis of 1H nuclear magnetic resonance spectra from human brain tumour biopsies. NMR Biomed. 11(4–5), 217–224 (1998)CrossRef
16.
Muni, D.P., Pal, N.R., Das, J.: Genetic programming for simultaneous feature selection and classifier design. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(1), 106–117 (2006)CrossRef
17.
18.
Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. London A Math. Phys. Eng. Sci. 454(1971), 903–995 (1998). The Royal SocietyCrossRefMathSciNetMATH
Metadata
Title
Predicting Acute Hypotensive Episodes Based on Multi GP
Authors
Dazhi Jiang
Bo Hu
Zhijian Wu
Copyright Year
2016
Publisher
Springer Singapore
Book
Computational Intelligence and Intelligent Systems

Print ISBN: 978-981-10-0355-4

Electronic ISBN: 978-981-10-0356-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-981-10-0356-1_16

Premium Partner

    Image Credits