2019
Peixoto, H.; Francisco, A.; Duarte, A.; Esteves, M.; Oliveira, S.; Lopes, V.; Abelha, A.; Machado, J.
Predicting Postoperative Complications for Gastric Cancer Patients Using Data Mining Proceedings Article
Em: L., Branco P. Portela C. F. Magalhaes (Ed.): pp. 37-46, Springer Verlag, 2019, ISSN: 18678211, (cited By 2; Conference of 10th International Conference on Intelligent Technologies for Interactive Entertainment, INTETAIN 2018 ; Conference Date: 21 November 2018 Through 23 November 2018; Conference Code:225079).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Decision making; Decision support systems; Diseases; Forecasting; Hospitals; Surgery, Clinical data; Clinical decision support systems; CRISP-DM; Decision making process; Gastric cancers; Healthcare environments; Postoperative complications; WEKA, Data mining
@inproceedings{Peixoto201937,
title = {Predicting Postoperative Complications for Gastric Cancer Patients Using Data Mining},
author = {H. Peixoto and A. Francisco and A. Duarte and M. Esteves and S. Oliveira and V. Lopes and A. Abelha and J. Machado},
editor = {Branco P. Portela C.F. Magalhaes L.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065038722&doi=10.1007%2f978-3-030-16447-8_4&partnerID=40&md5=86caef7589a1d51de058bdb33623a155},
doi = {10.1007/978-3-030-16447-8_4},
issn = {18678211},
year = {2019},
date = {2019-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {273},
pages = {37-46},
publisher = {Springer Verlag},
abstract = {Gastric cancer refers to the development of malign cells that can grow in any part of the stomach. With the vast amount of data being collected daily in healthcare environments, it is possible to develop new algorithms which can support the decision-making processes in gastric cancer patients treatment. This paper aims to predict, using the CRISP-DM methodology, the outcome from the hospitalization of gastric cancer patients who have undergone surgery, as well as the occurrence of postoperative complications during surgery. The study showed that, on one hand, the RF and NB algorithms are the best in the detection of an outcome of hospitalization, taking into account patients’ clinical data. On the other hand, the algorithms J48, RF, and NB offer better results in predicting postoperative complications. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019.},
note = {cited By 2; Conference of 10th International Conference on Intelligent Technologies for Interactive Entertainment, INTETAIN 2018 ; Conference Date: 21 November 2018 Through 23 November 2018; Conference Code:225079},
keywords = {Artificial intelligence; Decision making; Decision support systems; Diseases; Forecasting; Hospitals; Surgery, Clinical data; Clinical decision support systems; CRISP-DM; Decision making process; Gastric cancers; Healthcare environments; Postoperative complications; WEKA, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}