2015
Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.; Rua, F.; Silva, Á.
Real-time decision support using data mining to predict blood pressure critical events in intensive medicine patients Proceedings Article
Em: V., Hervas R. Bravo J. Villarreal (Ed.): pp. 77-90, Springer Verlag, 2015, ISSN: 03029743, (cited By 13; Conference of 1st International Conference on Ambient Intelligence for Health, AmIHEALTH 2015 ; Conference Date: 1 December 2015 Through 4 December 2015; Conference Code:159599).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Blood pressure; Decision support systems; Hospital data processing; Intensive care units, Continuous monitoring; Critical events; Decision supports; Intcare; Mining classification; Patient condition; Real time; Real time decisions, Data mining
@inproceedings{Portela201577,
title = {Real-time decision support using data mining to predict blood pressure critical events in intensive medicine patients},
author = {F. Portela and M. F. Santos and J. Machado and A. Abelha and F. Rua and Á. Silva},
editor = {Hervas R. Bravo J. Villarreal V.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954110962&doi=10.1007%2f978-3-319-26508-7_8&partnerID=40&md5=4b2b9925c5d4064a973f40f5a4636dff},
doi = {10.1007/978-3-319-26508-7_8},
issn = {03029743},
year = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9456},
pages = {77-90},
publisher = {Springer Verlag},
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%. © Springer International Publishing Switzerland 2015.},
note = {cited By 13; Conference of 1st International Conference on Ambient Intelligence for Health, AmIHEALTH 2015 ; Conference Date: 1 December 2015 Through 4 December 2015; Conference Code:159599},
keywords = {Artificial intelligence; Blood pressure; Decision support systems; Hospital data processing; Intensive care units, Continuous monitoring; Critical events; Decision supports; Intcare; Mining classification; Patient condition; Real time; Real time decisions, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
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%. © Springer International Publishing Switzerland 2015.