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Predicting plateau pressure in intensive medicine for ventilated patients

Bibliographic Details
Main Author: Oliveira, Sérgio
Publication Date: 2015
Other Authors: Portela, Filipe, Santos, Manuel, Machado, José Manuel, Abelha, António, Silva, Álvaro, Rua, Fernando
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/1822/39036
Summary: Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm2O) in a real environment and using real data. The present study explored and assessed the possibility of predicting the Plateau pressure class with high accuracies. The dataset used only contained data provided by the ventilators. The best models are able to predict the Plateau Pressure with an accuracy ranging from 95.52% to 98.71%.
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spelling Predicting plateau pressure in intensive medicine for ventilated patientsBarotraumaPlateau PressureIntensive MedicineData MiningINTCareMechanical VentilationScience & TechnologyBarotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm2O) in a real environment and using real data. The present study explored and assessed the possibility of predicting the Plateau pressure class with high accuracies. The dataset used only contained data provided by the ventilators. The best models are able to predict the Plateau Pressure with an accuracy ranging from 95.52% to 98.71%.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013. The authors would like to thank FCT (Foundation of Science and Technology, Portugal) for the financial support through the contract PTDC/EEI-SII/1302/2012 (INTCare II).SpringerUniversidade do MinhoOliveira, SérgioPortela, FilipeSantos, ManuelMachado, José ManuelAbelha, AntónioSilva, ÁlvaroRua, Fernando20152015-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/39036engOliveira, S., Portela, F., Santos, M. F., Machado, J., Abelha, A., Silva, Á., & Rua, F. (2015) Predicting plateau pressure in intensive medicine for ventilated patients. Vol. 354. Advances in Intelligent Systems and Computing (pp. 179-188).978-3-319-16527-12194-535710.1007/978-3-319-16528-8_17info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T04:44:54Zoai:repositorium.sdum.uminho.pt:1822/39036Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:57:20.128480Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Predicting plateau pressure in intensive medicine for ventilated patients
title Predicting plateau pressure in intensive medicine for ventilated patients
spellingShingle Predicting plateau pressure in intensive medicine for ventilated patients
Oliveira, Sérgio
Barotrauma
Plateau Pressure
Intensive Medicine
Data Mining
INTCare
Mechanical Ventilation
Science & Technology
title_short Predicting plateau pressure in intensive medicine for ventilated patients
title_full Predicting plateau pressure in intensive medicine for ventilated patients
title_fullStr Predicting plateau pressure in intensive medicine for ventilated patients
title_full_unstemmed Predicting plateau pressure in intensive medicine for ventilated patients
title_sort Predicting plateau pressure in intensive medicine for ventilated patients
author Oliveira, Sérgio
author_facet Oliveira, Sérgio
Portela, Filipe
Santos, Manuel
Machado, José Manuel
Abelha, António
Silva, Álvaro
Rua, Fernando
author_role author
author2 Portela, Filipe
Santos, Manuel
Machado, José Manuel
Abelha, António
Silva, Álvaro
Rua, Fernando
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Oliveira, Sérgio
Portela, Filipe
Santos, Manuel
Machado, José Manuel
Abelha, António
Silva, Álvaro
Rua, Fernando
dc.subject.por.fl_str_mv Barotrauma
Plateau Pressure
Intensive Medicine
Data Mining
INTCare
Mechanical Ventilation
Science & Technology
topic Barotrauma
Plateau Pressure
Intensive Medicine
Data Mining
INTCare
Mechanical Ventilation
Science & Technology
description Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm2O) in a real environment and using real data. The present study explored and assessed the possibility of predicting the Plateau pressure class with high accuracies. The dataset used only contained data provided by the ventilators. The best models are able to predict the Plateau Pressure with an accuracy ranging from 95.52% to 98.71%.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/39036
url http://hdl.handle.net/1822/39036
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Oliveira, S., Portela, F., Santos, M. F., Machado, J., Abelha, A., Silva, Á., & Rua, F. (2015) Predicting plateau pressure in intensive medicine for ventilated patients. Vol. 354. Advances in Intelligent Systems and Computing (pp. 179-188).
978-3-319-16527-1
2194-5357
10.1007/978-3-319-16528-8_17
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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repository.mail.fl_str_mv info@rcaap.pt
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