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A Neural Approach for Controlling Vital Signs in the Intensive Care Unit Patients

Ali Dadashi, Alireza Rowhanimanesh, Shadi Choupankareh
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Abstract

Controlling vital signs is crucial for patients in an intensive care unit (ICU) who need safe diagnostic and therapeutic interventions. The devices used in ICU should ensure accuracy, reliability and safety of alarms. The goal of personalized medicine in the ICU is to predict which diagnostic tests, monitoring interventions and treatments are necessary. In this study, we propose an intelligent approach based on artificial neural networks which is able to automatically learn the features of a patient and consequently send the required alarms in order to reduce the number of wrong alarms in ICU. Six of the most important risk factors are used and the importance of input variables is quantified by weighting according to expert’s knowledge. The data chosen for this study have been provided in a real ICU environment in the University of Queensland in Australia. The results demonstrate that the proposed neural approach can be used as an efficient method for controlling vital signs in a real ICU environment

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