2014
Pereira, E.; Brandão, A.; Salazar, M.; Portela, C. F.; Santos, M. F.; Machado, J.; Abelha, A.; Braga, J.
Pre-triage decision support improvement in maternity care by means of data mining Book Chapter
Em: pp. 175-192, IGI Global, 2014, ISBN: 9781466664784; 1466664770; 9781466664777, (cited By 3).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Data mining; Sensitivity analysis, Decision modeling; Decision support system (dss); Decision supports; Input variables; Management decisions; Manchester; Misclassifications; Waiting time, Decision support systems
@inbook{Pereira2014175,
title = {Pre-triage decision support improvement in maternity care by means of data mining},
author = {E. Pereira and A. Brandão and M. Salazar and C. F. Portela and M. F. Santos and J. Machado and A. Abelha and J. Braga},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946122274&doi=10.4018%2f978-1-4666-6477-7.ch009&partnerID=40&md5=aa8f639672408928c62c7d9bee639a7b},
doi = {10.4018/978-1-4666-6477-7.ch009},
isbn = {9781466664784; 1466664770; 9781466664777},
year = {2014},
date = {2014-01-01},
journal = {Integration of Data Mining in Business Intelligence Systems},
pages = {175-192},
publisher = {IGI Global},
abstract = {A triage system aims to make a correct characterization of the condition of patients. Because conventional triage systems like Manchester Triage System (MTS) are not suitable for maternity care, a decision model for pre-triaging patients in emergency (URG) and consultation (ARGO) classes was built and incorporated into a Decision Support System (DSS) implemented in Centro Materno Infantil do Norte (CMIN). Complementarily, DSS produces several indicators to support clinical and management decisions. A recent data analysis revealed a bias in the classification of URG cases. Frequently, cases classified as URG correspond to ARGO. This misclassification has been studied by means of Data Mining (DM) techniques in order to improve the pre-triage model and to discover knowledge for developing a new triage system based on waiting times and on a 5-scale of classes. This chapter presents a kind of sensitivity analysis combining input variables in six scenarios and considering four different DM techniques. CRISP-DM methodology was used to conduct the project. © 2015, IGI Global.},
note = {cited By 3},
keywords = {Artificial intelligence; Data mining; Sensitivity analysis, Decision modeling; Decision support system (dss); Decision supports; Input variables; Management decisions; Manchester; Misclassifications; Waiting time, Decision support systems},
pubstate = {published},
tppubtype = {inbook}
}
A triage system aims to make a correct characterization of the condition of patients. Because conventional triage systems like Manchester Triage System (MTS) are not suitable for maternity care, a decision model for pre-triaging patients in emergency (URG) and consultation (ARGO) classes was built and incorporated into a Decision Support System (DSS) implemented in Centro Materno Infantil do Norte (CMIN). Complementarily, DSS produces several indicators to support clinical and management decisions. A recent data analysis revealed a bias in the classification of URG cases. Frequently, cases classified as URG correspond to ARGO. This misclassification has been studied by means of Data Mining (DM) techniques in order to improve the pre-triage model and to discover knowledge for developing a new triage system based on waiting times and on a 5-scale of classes. This chapter presents a kind of sensitivity analysis combining input variables in six scenarios and considering four different DM techniques. CRISP-DM methodology was used to conduct the project. © 2015, IGI Global.