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TitleReal-Time models to predict the use of vasopressors in monitored patients
Author(s)Braga, A. C.
Portela, Filipe
Santos, Manuel
Abelha, António
Machado, José Manuel
Silva, Álvaro
Rua, Fernando
Intensive medicine
Data mining
Vital signs
Laboratory results
Issue date2016
JournalLecture Notes in Computer Science
Abstract(s)The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

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