DocumentCode :
2398274
Title :
IGUANA: Individuation of Global Unsafe ANomalies and Alarm activation
Author :
Apiletti, Daniele ; Baralis, Elena ; Bruno, Giulia ; Cerquitelli, Tania
Author_Institution :
Dipt. di Automatica e Informatica, Politecnico di Torino
fYear :
2006
fDate :
Sept. 2006
Firstpage :
267
Lastpage :
272
Abstract :
In this paper, we present the IGUANA (individuation of global unsafe anomalies and alarm activation) framework which performs analysis of clinical data to characterize the risk level of a patient and identify dangerous situations. Data mining techniques are exploited to build a model of both normal and unsafe situations, which can be tailored to specific behaviors of a given patient clinical situation. A risk function has been proposed to identify the instantaneous risk of each physiological parameter. The classification phase, performed on-line, assigns a risk label to each measured value. We have developed a prototype of IGUANA in R, an open source environment for statistical analyses and graphical visualization, to validate our approach. Experimental results, performed on 64 records of patients affected by different diseases, show the adaptability and the efficiency of the proposed approach
Keywords :
data mining; data visualisation; health care; patient monitoring; statistical analysis; alarm activation; clinical data analysis; data mining; global unsafe anomaly; graphical visualization; health care; open source environment; patient monitoring; statistical analysis; Data analysis; Data mining; Diseases; Performance analysis; Performance evaluation; Phase measurement; Prototypes; Risk analysis; Statistical analysis; Visualization; Data Mining; Healthcare; Patient Monitoring;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems, 2006 3rd International IEEE Conference on
Conference_Location :
London
Print_ISBN :
1-4244-01996-8
Electronic_ISBN :
1-4244-01996-8
Type :
conf
DOI :
10.1109/IS.2006.348429
Filename :
4155436
Link To Document :
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