2016
Braga, A.; Portela, F.; Santos, M. F.; Abelha, A.; Machado, J.; Silva, Á.; Rua, F.
Real-time models to predict the use of vasopressors in monitored patients Proceedings Article
Em: H., Zheng X. Zeng D. D. Chen (Ed.): pp. 15-25, Springer Verlag, 2016, ISSN: 03029743, (cited By 0; Conference of International Conference for Smart Health, ICSH 2015 ; Conference Date: 17 November 2015 Through 18 November 2015; Conference Code:163109).
Resumo | Links | BibTeX | Etiquetas: Data mining, Data mining models; Decision making process; INTCare; Laboratory analysis; Real time; Real-time data mining; Vasopressors; Vital sign, Decision making; Patient treatment
@inproceedings{Braga201615,
title = {Real-time models to predict the use of vasopressors in monitored patients},
author = {A. Braga and F. Portela and M. F. Santos and A. Abelha and J. Machado and Á. Silva and F. Rua},
editor = {Zheng X. Zeng D.D. Chen H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958580639&doi=10.1007%2f978-3-319-29175-8_2&partnerID=40&md5=4cd41eed311b3d67d7a3dfef7fa8c2d3},
doi = {10.1007/978-3-319-29175-8_2},
issn = {03029743},
year = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9545},
pages = {15-25},
publisher = {Springer Verlag},
abstract = {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. © Springer International Publishing Switzerland 2016.},
note = {cited By 0; Conference of International Conference for Smart Health, ICSH 2015 ; Conference Date: 17 November 2015 Through 18 November 2015; Conference Code:163109},
keywords = {Data mining, Data mining models; Decision making process; INTCare; Laboratory analysis; Real time; Real-time data mining; Vasopressors; Vital sign, Decision making; Patient treatment},
pubstate = {published},
tppubtype = {inproceedings}
}
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. © Springer International Publishing Switzerland 2016.