2013
Gonçalves, J. M. C.; Portela, F.; Santos, M. F.; Silva, Á.; Machado, J.; Abelha, A.
Predict sepsis level in intensive medicine - Data mining approach Proceedings Article
Em: pp. 201-211, Springer Verlag, Olhao, Algarve, 2013, ISSN: 21945357, (cited By 12; Conference of 2013 World Conference on Information Systems and Technologies, WorldCIST 2013 ; Conference Date: 27 March 2013 Through 30 March 2013; Conference Code:96582).
Resumo | Links | BibTeX | Etiquetas: Classification (of information); Decision making; Decision trees; Forecasting; Information systems; Intensive care units, Classification models; Confusion matrices; Data mining models; INTCare; Intensive care; Sepsis; Supervised learning approaches; Total error rates, Data mining
@inproceedings{Gonçalves2013201,
title = {Predict sepsis level in intensive medicine - Data mining approach},
author = {J. M. C. Gonçalves and F. Portela and M. F. Santos and Á. Silva and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876221428&doi=10.1007%2f978-3-642-36981-0_19&partnerID=40&md5=d5ca7bbcda25f0d1f2d0a30850f63245},
doi = {10.1007/978-3-642-36981-0_19},
issn = {21945357},
year = {2013},
date = {2013-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {206 AISC},
pages = {201-211},
publisher = {Springer Verlag},
address = {Olhao, Algarve},
abstract = {This paper aims to support doctor's decision-making on predicting the Sepsis level. Thus, a set of Data Mining (DM) models were developed using prevision techniques and classification models. These models enable a better doctor's decision having into account the Sepsis level of the patient. The DM models use real data collected from the Intensive Care Unit of the Santo António Hospital, in Oporto, Portugal. Classification DM models were considered to predict sepsis level in a supervised learning approach. The models were induced making use of the following algorithms: Decision Trees, Support Vector Machines and Naïve Bayes classifier. The models were assessed using the Confusion Matrix, associated metrics, and Cross-validation. The analysis of the total error rate, sensitivity, specificity and accuracy were the metrics used to identify the most relevant measures to predict sepsis level. This work demonstrates that it is possible to predict with great accuracy the sepsis level. © 2013 Springer-Verlag.},
note = {cited By 12; Conference of 2013 World Conference on Information Systems and Technologies, WorldCIST 2013 ; Conference Date: 27 March 2013 Through 30 March 2013; Conference Code:96582},
keywords = {Classification (of information); Decision making; Decision trees; Forecasting; Information systems; Intensive care units, Classification models; Confusion matrices; Data mining models; INTCare; Intensive care; Sepsis; Supervised learning approaches; Total error rates, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper aims to support doctor's decision-making on predicting the Sepsis level. Thus, a set of Data Mining (DM) models were developed using prevision techniques and classification models. These models enable a better doctor's decision having into account the Sepsis level of the patient. The DM models use real data collected from the Intensive Care Unit of the Santo António Hospital, in Oporto, Portugal. Classification DM models were considered to predict sepsis level in a supervised learning approach. The models were induced making use of the following algorithms: Decision Trees, Support Vector Machines and Naïve Bayes classifier. The models were assessed using the Confusion Matrix, associated metrics, and Cross-validation. The analysis of the total error rate, sensitivity, specificity and accuracy were the metrics used to identify the most relevant measures to predict sepsis level. This work demonstrates that it is possible to predict with great accuracy the sepsis level. © 2013 Springer-Verlag.