2011
Portela, F.; Santos, M. F.; Gago, P.; Silva, A.; Rua, F.; Abelha, A.; Machado, J.; Neves, J.
Enabling real-time intelligent decision support in intensive care Proceedings Article
Em: pp. 419-426, EUROSIS, Guimaraes, 2011, ISBN: 9789077381663, (cited By 15; Conference of 25th European Simulation and Modelling Conference, ESM 2011 ; Conference Date: 24 October 2011 Through 26 October 2011; Conference Code:104378).
Resumo | Links | BibTeX | Etiquetas: Acquisition process; Continuous monitoring; Data engineering; Data transformation; Intelligent decision support; KDD; Prediction model; Real-time, Agents; Biomedical equipment; Data acquisition; Decision support systems; Intelligent agents; Intensive care units; Modal analysis, Data mining
@inproceedings{Portela2011419,
title = {Enabling real-time intelligent decision support in intensive care},
author = {F. Portela and M. F. Santos and P. Gago and A. Silva and F. Rua and A. Abelha and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898952827&partnerID=40&md5=c145f0e0582ad54ee762733fb006d977},
isbn = {9789077381663},
year = {2011},
date = {2011-01-01},
journal = {ESM 2011 - 2011 European Simulation and Modelling Conference: Modelling and Simulation 2011},
pages = {419-426},
publisher = {EUROSIS},
address = {Guimaraes},
abstract = {Medical devices in ICU allow for both continuous monitoring of patients and data collection. Nevertheless, the amount of data to be considered is such that it is difficult for doctors to extract all the useful knowledge. In order to help uncover some of that knowledge we have built an IDSS based in the agent's paradigm and using data mining techniques to build prediction models. With the intention of collecting as much data as possible the data acquisition process was automated. Furthermore, given the paramount importance of data quality for data mining a data quality agent responsible for detecting the errors in the data was devised. Indeed, data acquisition in the ICU is error prone as, for instance, sensors may be displaced as patients move. The aim of this paper is to present the overall KDD process implemented, presenting in detail the data transformations that were done and the benefits achieved. ©2011 EUROSIS-ETI.},
note = {cited By 15; Conference of 25th European Simulation and Modelling Conference, ESM 2011 ; Conference Date: 24 October 2011 Through 26 October 2011; Conference Code:104378},
keywords = {Acquisition process; Continuous monitoring; Data engineering; Data transformation; Intelligent decision support; KDD; Prediction model; Real-time, Agents; Biomedical equipment; Data acquisition; Decision support systems; Intelligent agents; Intensive care units; Modal analysis, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
Medical devices in ICU allow for both continuous monitoring of patients and data collection. Nevertheless, the amount of data to be considered is such that it is difficult for doctors to extract all the useful knowledge. In order to help uncover some of that knowledge we have built an IDSS based in the agent's paradigm and using data mining techniques to build prediction models. With the intention of collecting as much data as possible the data acquisition process was automated. Furthermore, given the paramount importance of data quality for data mining a data quality agent responsible for detecting the errors in the data was devised. Indeed, data acquisition in the ICU is error prone as, for instance, sensors may be displaced as patients move. The aim of this paper is to present the overall KDD process implemented, presenting in detail the data transformations that were done and the benefits achieved. ©2011 EUROSIS-ETI.