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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Caetano, Nuno, Laureano, Raul M. S. & Cortez, Paulo (2014). A Data-driven Approach to Predict Hospital Length of Stay - A Portuguese Case Study. Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014). 1, 407-414
Exportar Referência (IEEE) 
N. Caetano et al.,  "A Data-driven Approach to Predict Hospital Length of Stay - A Portuguese Case Study", in Proc. of the 16th Int. Conf. on Enterprise Information Systems (ICEIS 2014), Lisboa/Portugal, vol. 1, pp. 407-414, 2014
Exportar BibTeX 
@misc{caetano2014_1734885354711,
	author = "Caetano, Nuno and Laureano, Raul M. S. and Cortez, Paulo",
	title = "A Data-driven Approach to Predict Hospital Length of Stay - A Portuguese Case Study",
	year = "2014",
	howpublished = "Ambos (impresso e digital)",
	url = "http://www.iceis.org"
}
Exportar RIS 
TY  - CPAPER
TI  - A Data-driven Approach to Predict Hospital Length of Stay - A Portuguese Case Study
T2  - Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014)
VL  - 1
AU  - Caetano, Nuno
AU  - Laureano, Raul M. S.
AU  - Cortez, Paulo
PY  - 2014
SP  - 407-414
CY  - Lisboa/Portugal
UR  - http://www.iceis.org
AB  - Data Mining (DM) aims at the extraction of useful knowledge from raw data. In the last decades, hospitals
have collected large amounts of data through new methods of electronic data storage, thus increasing the
potential value of DM in this domain area, in what is known as medical data mining. This work focuses on
the case study of a Portuguese hospital, based on recent and large dataset that was collected from 2000 to 2013. A data-driven predictive model was obtained for the length of stay (LOS), using as inputs indicators
commonly available at the hospitalization process. Based on a regression approach, several state-of-the-art DM models were compared. The best result was obtained by a Random Forest (RF), which presents a high quality coefficient of determination value (0.81). Moreover, a sensitivity analysis approach was used to extract human understandable knowledge from the RF model, revealing top three influential input attributes: hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such predictive and explanatory knowledge is valuable for supporting decisions of hospital managers.
ER  -