2019
Machado, J.; Cardoso, A. C.; Gomes, I.; Silva, I.; Lopes, V.; Peixoto, H.; Abelha, A.
Predicting the Length of Hospital Stay After Surgery for Perforated Peptic Ulcer Proceedings Article
Em: M., Rocha A. Ferras C. Paredes (Ed.): pp. 569-579, Springer Verlag, 2019, ISSN: 21945357, (cited By 1; Conference of International Conference on Information Technology and Systems, ICITS 2019 ; Conference Date: 6 February 2019 Through 8 February 2019; Conference Code:223499).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Classification (of information); Decision support systems; Diseases; Forecasting; Hospitals; Surgery, Classification models; CRISP-DM; Data mining process; Length of hospital stays; Peptic ulcer disease; Peptic ulcers; Rule based algorithms; Surgical procedures, Data mining
@inproceedings{Machado2019569,
title = {Predicting the Length of Hospital Stay After Surgery for Perforated Peptic Ulcer},
author = {J. Machado and A. C. Cardoso and I. Gomes and I. Silva and V. Lopes and H. Peixoto and A. Abelha},
editor = {Rocha A. Ferras C. Paredes M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061357086&doi=10.1007%2f978-3-030-11890-7_55&partnerID=40&md5=c2f374f26c35d638295694daa029c557},
doi = {10.1007/978-3-030-11890-7_55},
issn = {21945357},
year = {2019},
date = {2019-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {918},
pages = {569-579},
publisher = {Springer Verlag},
abstract = {The management of peptic ulcer disease usually implies an urgent surgical procedure with the need of a patient’s hospital admission. By predicting the length of hospital stay of patients, improvements can be made regarding the quality of services provided to patients. This paper focuses on using real data to identify patterns in patients’ profiles and surgical events, in order to predict if patients will need hospital care for a shorter or longer period of time. This goal is pursued using a Data Mining process which follows the CRISP-DM methodology. In particular, classification models are built by combining different scenarios, algorithms and sampling methods. The data mining model which performed best achieved an accuracy of 87.30%, a specificity of 89.40%, and a sensitivity of 81.30%, using JRip, a rule-based algorithm and Cross Validation as a sampling method. © 2019, Springer Nature Switzerland AG.},
note = {cited By 1; Conference of International Conference on Information Technology and Systems, ICITS 2019 ; Conference Date: 6 February 2019 Through 8 February 2019; Conference Code:223499},
keywords = {Artificial intelligence; Classification (of information); Decision support systems; Diseases; Forecasting; Hospitals; Surgery, Classification models; CRISP-DM; Data mining process; Length of hospital stays; Peptic ulcer disease; Peptic ulcers; Rule based algorithms; Surgical procedures, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}