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Towards an ICU Clinical Decision Support System using Data Wavelets

Published on None 2011 by Apkar Salatian, Francis Adepoju
Intelligent Systems and Data Processing
Foundation of Computer Science USA
ICISD - Number 1
None 2011
Authors: Apkar Salatian, Francis Adepoju
125cde66-0c29-4947-b22f-f01da4dd3079

Apkar Salatian, Francis Adepoju . Towards an ICU Clinical Decision Support System using Data Wavelets. Intelligent Systems and Data Processing. ICISD, 1 (None 2011), 37-43.

@article{
author = { Apkar Salatian, Francis Adepoju },
title = { Towards an ICU Clinical Decision Support System using Data Wavelets },
journal = { Intelligent Systems and Data Processing },
issue_date = { None 2011 },
volume = { ICISD },
number = { 1 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 37-43 },
numpages = 7,
url = { /specialissues/icisd/number1/2316-26/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Intelligent Systems and Data Processing
%A Apkar Salatian
%A Francis Adepoju
%T Towards an ICU Clinical Decision Support System using Data Wavelets
%J Intelligent Systems and Data Processing
%@ 0975-8887
%V ICISD
%N 1
%P 37-43
%D 2011
%I International Journal of Computer Applications
Abstract

Effective management of device-supported patients in the Intensive Care Unit (ICU) is complex, involving the interpretation of large volumes of high frequency data from numerous cardiac and respiratory parameters presented by the ICU monitors. ICU Clinical Decision Support systems can play an important role in assisting medical staff in terms of its ability to disentangle and comprehend large amount of physiological datasets with a number of explanatory variables. We propose data wavelets as a data mining approach for analyzing historical ICU data for deriving trends. We propose a clinical decision support system that uses the trends to assist medical staff by performing temporal reasoning to determine the outcome of therapies and to reason qualitatively to remove clinically insignificant events and to identify clinical conditions.

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Index Terms

Computer Science
Information Sciences

Keywords

signal processing medicine time-series analysis data mining wavelets