Neuro-fuzzy icu ventilator patients modeling

Artificial ventilation is an important treatment for the patients in the intensive care unit (ICU). An important step in building an efficient decision support system for intelligent ventilation is to develop a patient model. The patient model gives an idea of how the patient will behave under diffe...

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Main Authors:	Abdul Rahman, Abdul Wahab, Quek, Chai, A. H., Luah
Format:	Article
Language:	English
Published:	2009
Subjects:	T Technology (General) T58.5 Information technology
Online Access:	http://irep.iium.edu.my/9572/ http://irep.iium.edu.my/9572/1/Neuro-fuzzy_Icu_Ventilator.pdf

id	iium-9572
recordtype	eprints
spelling	iium-95722012-04-27T02:01:08Z http://irep.iium.edu.my/9572/ Neuro-fuzzy icu ventilator patients modeling Abdul Rahman, Abdul Wahab Quek, Chai A. H., Luah T Technology (General) T58.5 Information technology Artificial ventilation is an important treatment for the patients in the intensive care unit (ICU). An important step in building an efficient decision support system for intelligent ventilation is to develop a patient model. The patient model gives an idea of how the patient will behave under different ventilator settings. The feasibility of using neuro-fuzzy systems to model the physiology of the ventilated patient was assessed. Feature selection was also performed to explore other important input variables that could model the physiological state of the patients more accurately. Even though the overall patient model do not provide a good matched prediction of the patient's arterial oxygen tension, but it is still able to provide satisfactory arterial carbon dioxide tension predictions. This thus opens up new opportunity to model the ventilated patient. 2009 Article PeerReviewed application/pdf en http://irep.iium.edu.my/9572/1/Neuro-fuzzy_Icu_Ventilator.pdf Abdul Rahman, Abdul Wahab and Quek, Chai and A. H., Luah (2009) Neuro-fuzzy icu ventilator patients modeling. Journal of Computational Methods in Sciences and Engineering , 9 (2). S159-S167.
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General) T58.5 Information technology
spellingShingle	T Technology (General) T58.5 Information technology Abdul Rahman, Abdul Wahab Quek, Chai A. H., Luah Neuro-fuzzy icu ventilator patients modeling
description	Artificial ventilation is an important treatment for the patients in the intensive care unit (ICU). An important step in building an efficient decision support system for intelligent ventilation is to develop a patient model. The patient model gives an idea of how the patient will behave under different ventilator settings. The feasibility of using neuro-fuzzy systems to model the physiology of the ventilated patient was assessed. Feature selection was also performed to explore other important input variables that could model the physiological state of the patients more accurately. Even though the overall patient model do not provide a good matched prediction of the patient's arterial oxygen tension, but it is still able to provide satisfactory arterial carbon dioxide tension predictions. This thus opens up new opportunity to model the ventilated patient.
format	Article
author	Abdul Rahman, Abdul Wahab Quek, Chai A. H., Luah
author_facet	Abdul Rahman, Abdul Wahab Quek, Chai A. H., Luah
author_sort	Abdul Rahman, Abdul Wahab
title	Neuro-fuzzy icu ventilator patients modeling
title_short	Neuro-fuzzy icu ventilator patients modeling
title_full	Neuro-fuzzy icu ventilator patients modeling
title_fullStr	Neuro-fuzzy icu ventilator patients modeling
title_full_unstemmed	Neuro-fuzzy icu ventilator patients modeling
title_sort	neuro-fuzzy icu ventilator patients modeling
publishDate	2009
url	http://irep.iium.edu.my/9572/ http://irep.iium.edu.my/9572/1/Neuro-fuzzy_Icu_Ventilator.pdf
first_indexed	2023-09-18T20:19:14Z
last_indexed	2023-09-18T20:19:14Z
_version_	1777407994760265728

Neuro-fuzzy icu ventilator patients modeling

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