In this chapter we present the IGUANA (real time Individuation of Global Unsafe Anomalies and Alarm activation) framework which performs real-time analysis of clinical data to assess the instantaneous risk of a patient and identify dangerous situations. The proposed approach consists of two phases. As a first step, historical data is analyzed to build a model of both normal and unsafe situations, which can be tailored to specific behaviors of a given patient clinical situation. The model exploits a risk function to characterize the risk level of a patient by analyzing his/her vital signs. Then, an online classification phase is performed. A risk label is assigned to each measure by applying the most suitable model and an alarm is triggered for dangerous situations. To allow ubiquitous analysis, this step has been developed to run on mobile devices and its performance has been evaluated on both smart phone and personal computer. Experimental results, performed on 64 records of patients affected by different diseases, show the adaptability and the efficiency of the proposed approach.

Real-time Individuation of Global Unsafe Anomalies and Alarm Activation / Apiletti, Daniele; Baralis, ELENA MARIA; Bruno, Giulia; Cerquitelli, Tania (STUDIES IN COMPUTATIONAL INTELLIGENCE). - In: Intelligent Techniques and Tools for Novel System Architectures / Chountas, P.; Petrounias, I.; Kacprzyk, J.. - STAMPA. - [s.l] : Springer Berlin Heidelberg, 2008. - ISBN 9783540776215. - pp. 220-236 [10.1007/978-3-540-77623-9_13]

Real-time Individuation of Global Unsafe Anomalies and Alarm Activation

APILETTI, DANIELE;BARALIS, ELENA MARIA;BRUNO, GIULIA;CERQUITELLI, TANIA
2008

Abstract

In this chapter we present the IGUANA (real time Individuation of Global Unsafe Anomalies and Alarm activation) framework which performs real-time analysis of clinical data to assess the instantaneous risk of a patient and identify dangerous situations. The proposed approach consists of two phases. As a first step, historical data is analyzed to build a model of both normal and unsafe situations, which can be tailored to specific behaviors of a given patient clinical situation. The model exploits a risk function to characterize the risk level of a patient by analyzing his/her vital signs. Then, an online classification phase is performed. A risk label is assigned to each measure by applying the most suitable model and an alarm is triggered for dangerous situations. To allow ubiquitous analysis, this step has been developed to run on mobile devices and its performance has been evaluated on both smart phone and personal computer. Experimental results, performed on 64 records of patients affected by different diseases, show the adaptability and the efficiency of the proposed approach.
2008
9783540776215
Intelligent Techniques and Tools for Novel System Architectures
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1665208
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo