2016
Peixoto, R.; Portela, F.; Pinto, F.; Santos, M. F.; Machado, J.; Abelha, A.; Rua, F.
Resurgery Clusters in Intensive Medicine Proceedings Article
Em: E., Shakshuki (Ed.): pp. 528-533, Elsevier B.V., 2016, ISSN: 18770509, (cited By 0; Conference of 7th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2016 / The 6th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH-2016 / Affiliated Workshops, 2016 ; Conference Date: 19 September 2016 Through 22 September 2016; Conference Code:131700).
Resumo | Links | BibTeX | Etiquetas: Clustering; Clustering techniques; Critical care medicine; Electronic medical record; INTCare; Intervention; Predictive models; Surgical interventions, Data mining; Health care; Intensive care units; Medical computing; Medicine; Surgery, Patient treatment
@inproceedings{Peixoto2016528,
title = {Resurgery Clusters in Intensive Medicine},
author = {R. Peixoto and F. Portela and F. Pinto and M. F. Santos and J. Machado and A. Abelha and F. Rua},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992435562&doi=10.1016%2fj.procs.2016.09.072&partnerID=40&md5=c079429a47641fbe04385e2e5678042a},
doi = {10.1016/j.procs.2016.09.072},
issn = {18770509},
year = {2016},
date = {2016-01-01},
journal = {Procedia Computer Science},
volume = {58},
pages = {528-533},
publisher = {Elsevier B.V.},
abstract = {The field of critical care medicine is confronted every day with cases of surgical interventions. When Data Mining is properly applied in this field, it is possible through predictive models to identify if a patient, should or should not have surgery again upon the same problem. The goal of this work is to apply clustering techniques in collected data in order to categorize re-interventions in intensive care. By knowing the common characteristics of the re-intervention patients it will be possible to help the physician to predict a future resurgery. For this study various attributes were used related to the patient's health problems like heart problems or organ failure. For this study it was also considered important aspects such as age and what type of surgery the patient was submitted. Classes were created with the patients' age and the number of days after the first surgery. Another class was created where the type of surgery that the patient was operated upon was identified. This study comprised Davies Bouldin values between -0.977 and -0.416. The used variables, in addition to being provided by Hospital de Santo António in Porto, they are provided from the electronic medical record. © 2016 The Authors.},
note = {cited By 0; Conference of 7th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2016 / The 6th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH-2016 / Affiliated Workshops, 2016 ; Conference Date: 19 September 2016 Through 22 September 2016; Conference Code:131700},
keywords = {Clustering; Clustering techniques; Critical care medicine; Electronic medical record; INTCare; Intervention; Predictive models; Surgical interventions, Data mining; Health care; Intensive care units; Medical computing; Medicine; Surgery, Patient treatment},
pubstate = {published},
tppubtype = {inproceedings}
}
The field of critical care medicine is confronted every day with cases of surgical interventions. When Data Mining is properly applied in this field, it is possible through predictive models to identify if a patient, should or should not have surgery again upon the same problem. The goal of this work is to apply clustering techniques in collected data in order to categorize re-interventions in intensive care. By knowing the common characteristics of the re-intervention patients it will be possible to help the physician to predict a future resurgery. For this study various attributes were used related to the patient's health problems like heart problems or organ failure. For this study it was also considered important aspects such as age and what type of surgery the patient was submitted. Classes were created with the patients' age and the number of days after the first surgery. Another class was created where the type of surgery that the patient was operated upon was identified. This study comprised Davies Bouldin values between -0.977 and -0.416. The used variables, in addition to being provided by Hospital de Santo António in Porto, they are provided from the electronic medical record. © 2016 The Authors.