Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/41708

TitleReal-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients
Author(s)Portela, Filipe
Santos, Manuel
Machado, José Manuel
Abelha, António
Rua, Fernando
Silva, Álvaro
KeywordsData mining
INTCare
Intensive medicine
Blood pressure
Critical events
Decision support
Real-Time
Issue date2015
PublisherSpringer
JournalLecture Notes in Computer Science
Abstract(s)Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.
TypeConference paper
URIhttp://hdl.handle.net/1822/41708
ISBN978-3-319-26507-0
DOI10.1007/978-3-319-26508-7_8
ISSN0302-9743
Publisher versionhttp://link.springer.com/chapter/10.1007%2F978-3-319-26508-7_8
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

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