2015
Pereira, S.; Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.
Predicting preterm birth in maternity care by means of data mining Proceedings Article
Em: F., Costa E. Machado P. Pereira (Ed.): pp. 116-121, Springer Verlag, 2015, ISSN: 03029743, (cited By 1; Conference of 17th Portuguese Conference on Artificial Intelligence, EPIA 2015 ; Conference Date: 8 September 2015 Through 11 September 2015; Conference Code:140439).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Decision making; Forecasting; Obstetrics, Data mining, Data mining models; Decision making process; Maternity care; Preterm birth; Preterm deliveries; Real data; Real environments; Sensitivity and specificity
@inproceedings{Pereira2015116,
title = {Predicting preterm birth in maternity care by means of data mining},
author = {S. Pereira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Costa E. Machado P. Pereira F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945926609&doi=10.1007%2f978-3-319-23485-4_12&partnerID=40&md5=49364e9e0ed601bbcecbef6c655affb4},
doi = {10.1007/978-3-319-23485-4_12},
issn = {03029743},
year = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9273},
pages = {116-121},
publisher = {Springer Verlag},
abstract = {Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively. © Springer International Publishing Switzerland 2015.},
note = {cited By 1; Conference of 17th Portuguese Conference on Artificial Intelligence, EPIA 2015 ; Conference Date: 8 September 2015 Through 11 September 2015; Conference Code:140439},
keywords = {Artificial intelligence; Decision making; Forecasting; Obstetrics, Data mining, Data mining models; Decision making process; Maternity care; Preterm birth; Preterm deliveries; Real data; Real environments; Sensitivity and specificity},
pubstate = {published},
tppubtype = {inproceedings}
}
Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively. © Springer International Publishing Switzerland 2015.