2015
Pereira, S.; Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.
Predicting Type of Delivery by Identification of Obstetric Risk Factors through Data Mining Proceedings Article
Em: V.J., Cruz-Cunha M. M. Eduardo (Ed.): pp. 601-609, Elsevier B.V., 2015, ISSN: 18770509, (cited By 32; 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: Data mining models; Delivery techniques; High quality service; Maternity Care; Pregnant; Real data;Obstetrics Care; Sensitivity and specificity; Type of delivery, Data mining; Forecasting; Information systems; Interoperability; Obstetrics; Project management, Information management
@inproceedings{Pereira2015601,
title = {Predicting Type of Delivery by Identification of Obstetric Risk Factors through Data Mining},
author = {S. Pereira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Cruz-Cunha M. M. Eduardo V.J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962855663&doi=10.1016%2fj.procs.2015.08.573&partnerID=40&md5=cc8176cfd884fad22b2f8f3993869258},
doi = {10.1016/j.procs.2015.08.573},
issn = {18770509},
year = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {64},
pages = {601-609},
publisher = {Elsevier B.V.},
abstract = {In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries. © 2015 The Authors. Published by Elsevier B.V.},
note = {cited By 32; 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 = {Data mining models; Delivery techniques; High quality service; Maternity Care; Pregnant; Real data;Obstetrics Care; Sensitivity and specificity; Type of delivery, Data mining; Forecasting; Information systems; Interoperability; Obstetrics; Project management, Information management},
pubstate = {published},
tppubtype = {inproceedings}
}
In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries. © 2015 The Authors. Published by Elsevier B.V.