Clustering Barotrauma Patients in ICU–A Data Mining Based Approach Using Ventilator Variables

Oliveira, Sérgio Manuel Costa; Portela, Filipe; Santos, Manuel Filipe; Machado, José Cunha; Abelha, António; Silva, Álvaro; Rua, Fernando

doi:10.1007/978-3-319-23485-4_13

“…Now this project is focused in the respiratory system and in predicting the occurrence of barotrauma [17]. With the development of this new approach and the definition of critical events to the ventilation, the influence of these variables will be studied in the models already induced [18][19][20].…”

Section: Intcarementioning

confidence: 99%

Critical Events in Mechanically Ventilated Patients

Portela

¹

,

Santos

²

,

Machado

³

et al. 2016

New Advances in Information Systems and Technologies

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Abstract. Mechanical Ventilation is an artificial way to help a Patient to breathe. This procedure is used to support patients with respiratory diseases however in many cases it can provoke lung damages, Acute Respiratory Diseases or organ failure. With the goal to early detect possible patient breath problems a set of limit values was defined to some variables monitored by the ventilator (Average Ventilation Pressure, Compliance Dynamic, Flow, Peak, Plateau and Support Pressure, Positive end-expiratory pressure, Respiratory Rate) in order to create critical events. A critical event is verified when a patient has a value higher or lower than the normal range defined for a certain period of time. The values were defined after elaborate a literature review and meeting with physicians specialized in the area. This work uses data streaming and intelligent agents to process the values collected in real-time and classify them as critical or not. Real data provided by an Intensive Care Unit were used to design and test the solution. In this study it was possible to understand the importance of introduce critical events for Mechanically Ventilated Patients. In some cases a value is considered critical (can trigger an alarm) however it is a single event (instantaneous) and it has not a clinical significance for the patient. The introduction of critical events which crosses a range of values and a predefined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition.

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Data Quality and Critical Events in Ventilation

Portela

¹

,

Santos

²

,

Abelha

³

et al. 2020

Hospital Management and Emergency Medicine

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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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Data Quality and Critical Events in Ventilation

Machado

¹

,

Abelha

²

,

Rua

³

et al. 2017

International Journal of Reliable and Quality E-Healthcare

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0

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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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Clustering Barotrauma Patients in ICU–A Data Mining Based Approach Using Ventilator Variables

Cited by 3 publications

References 6 publications

Critical Events in Mechanically Ventilated Patients

Critical Events in Mechanically Ventilated Patients

Data Quality and Critical Events in Ventilation

Data Quality and Critical Events in Ventilation

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