Paper Info
Title
Real-Time Decision Support Using Data Mining to Predict Blood Pressure Critical Events in Intensive Medicine Patients.
Abstract
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 %.
Year
DOI
Venue
2015
10.1007/978-3-319-26508-7_8
AMBIENT INTELLIGENCE FOR HEALTH, AMIHEALTH 2015
Keywords
Field
DocType
Data mining,Intcare,Intensive medicine,Blood pressure,Critical events,Decision support,Real-Time
Data mining,Computer science,Decision support system,Vital signs,Intensivist,Continuous monitoring,Blood pressure,Intensive care
Conference
Volume
ISSN
Citations 
9456
0302-9743
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Filipe Portela117744.10
Manuel Filipe Santos236068.91
José Machado320734.92
António Abelha424357.30
Fernando Rua57815.32
Álvaro M. Silva612518.39