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TitleIntelligent decision support to predict patient barotrauma risk in intensive care units
Author(s)Oliveira, Sérgio Manuel Costa
Portela, Filipe
Santos, Manuel Filipe
Machado, José Manuel
Abelha, António
Silva, Álvaro
Rua, Fernando
Intensive care
Data mining
Mechanical ventilation
Decision support
Issue date2015
JournalProcedia Computer Science
Abstract(s)The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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