Data Quality and Critical Events in Ventilation: An Intensive Care study

Data Quality and Critical Events in Ventilation: An Intensive Care study

Filipe Portela, Manuel Filipe Santos, António Abelha, José Machado, Fernando Rua
Copyright: © 2017 |Pages: 9
DOI: 10.4018/IJRQEH.2017040104
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The data quality assessment is a critical task in Intensive Care Units (ICUs). In the ICUs the patients are continuously monitored and the values are collected in real-time through data streaming processes. In the case of ventilation, the ventilator is monitoring the patient respiratory system and then a gateway receives the monitored values. This process can collect any values, noise values or values that can have clinical significance, for example, when a patient is having a critical event associated with the respiratory system. In this paper, the critical events concept was applied to the ventilation system, and a quality assessment of the collected data was performed when a new value arrived. Some interesting results were achieved: 56.59% of the events were critical, and 5% of the data collected were noise values. In this field, Average Ventilation Pressure and Peak flow are respectively the variables with the most influence.
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Mechanical Ventilation

A patient needs to be connected to a ventilator when he cannot breathe. In this work, the mechanical ventilation (artificial) is used. Mechanical ventilation in Intensive Care Units is considered an essential, life-saving therapy for patients with critical illness and respiratory failures (Prevention, 2015). According to Evans (Evans et al., 2005), the ventilators were also developed to generate alarms when a patient becomes disconnected, or the ventilation values are critical. Despite these innovations, the ventilator is not capable of determining if a value is valid or not, i.e., if the patient presents a normal value or a noise value or how critical is the collected value. A valid analysis requires some human observations. However, the humans can only see the collected values, and it is tough to analyse several values in a few minutes. To minimize this problem a set of intelligent procedures, able to detect the data quality, can be defined. These procedures are usually based on the use of intelligent agents (Cardoso et al., 2014). In this field, a set of experiments was made to define and detect critical events in the respiratory system (Portela, Gago, Santos, Silva, & Rua, 2012; Portela, Gago, et al., 2013; Portela et al., 2016).

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