pubs2024

@inproceedings{Fonseca2017565,

title = {Step Towards Prediction of Perineal Tear},

author = {F. Fonseca and H. Peixoto and F. Miranda and J. Machado and A. Abelha},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033453791&doi=10.1016%2fj.procs.2017.08.284&partnerID=40&md5=48288649c62db1ddede9cdc0711b5b5d},

doi = {10.1016/j.procs.2017.08.284},

issn = {18770509},

year  = {2017},

date = {2017-01-01},

journal = {Procedia Computer Science},

volume = {113},

pages = {565-570},

publisher = {Elsevier B.V.},

abstract = {The aim of this study is to predict, through data mining tools, the incidence of perineal tear. This kind of laceration developed during child delivery might imply surgery and entails a set of several consequences. Clinical Decision Support Systems, with the information collected from patients' electronic health records combined with the data mining techniques, may decrease the incidence of perineal tears during labour. Peer-review under responsibility of the Conference Program Chairs. © 2017 The Authors. Published by Elsevier B.V.},

note = {cited By 6; Conference of 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2017 and the 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2017 ; Conference Date: 18 September 2017 Through 20 September 2017; Conference Code:130912},

keywords = {Artificial intelligence; Data communication systems; Decision support systems; Health care; Obstetrics, Clinical decision support systems; Conference programs; Data-mining tools; Electronic health record; Peer review; Perineal Tear, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}