2015
Oliveira, S.; Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.; Silva, Á.; Rua, F.
Intelligent Decision Support to Predict Patient Barotrauma Risk in Intensive Care Units Proceedings Article
Em: V.J., Cruz-Cunha M. M. Eduardo (Ed.): pp. 626-634, Elsevier B.V., 2015, ISSN: 18770509, (cited By 7; Conference of Conference on ENTERprise Information Systems/International Conference on Project MANagement/Conference on Health and Social Care Information Systems and Technologies, CENTERIS 2015 ; Conference Date: 7 October 2015 Through 9 October 2015; Conference Code:123098).
Resumo | Links | BibTeX | Etiquetas: Barotrauma; Decision supports; INTCare; Intensive care; Mechanical ventilation; Patient-centered, Data mining, Decision support systems; Decision trees; Forecasting; Health; Information management; Information systems; Intensive care units; Probability; Project management; Risks; Ventilation
@inproceedings{Oliveira2015626,
title = {Intelligent Decision Support to Predict Patient Barotrauma Risk in Intensive Care Units},
author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
editor = {Cruz-Cunha M. M. Eduardo V.J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962786672&doi=10.1016%2fj.procs.2015.08.576&partnerID=40&md5=9da1a81f449b781b145fd534e71f6c96},
doi = {10.1016/j.procs.2015.08.576},
issn = {18770509},
year = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {64},
pages = {626-634},
publisher = {Elsevier B.V.},
abstract = {The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: "risk" and "no risk". Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated. © 2015 The Authors. Published by Elsevier B.V.},
note = {cited By 7; Conference of Conference on ENTERprise Information Systems/International Conference on Project MANagement/Conference on Health and Social Care Information Systems and Technologies, CENTERIS 2015 ; Conference Date: 7 October 2015 Through 9 October 2015; Conference Code:123098},
keywords = {Barotrauma; Decision supports; INTCare; Intensive care; Mechanical ventilation; Patient-centered, Data mining, Decision support systems; Decision trees; Forecasting; Health; Information management; Information systems; Intensive care units; Probability; Project management; Risks; Ventilation},
pubstate = {published},
tppubtype = {inproceedings}
}
The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: "risk" and "no risk". Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated. © 2015 The Authors. Published by Elsevier B.V.