Predicting Nosocomial Infection by Using Data Mining Technologies - New Contributions in Information Systems and Technologies - page 185

Acknowledgments. This work has been supported by FCT - Fundação para a Ciência
e Tecnologia within the Project Scope UID/CEC/00319/2013.
References
1. Clean Care is Safer Care Team: Report on the burden of endemic health care-associated
infection worldwide: Clean care is safer care. Technical report, World Health Organization
(2011)
2. Inweregbu, K., Dave, J., Pittard, A.: Nosocomial infections. Continuing Education in
Anaesthesia. Critical Care & Pain 5(1), 14-17 (2005)
3. Rigor, H., Machado, J., Abelha, A., Neves, J., Alberto, C.: A web-based system to reduce
the nosocomial infection impact in healthcare units. In: Proceedings of the WEBIST 2008
- International Conference on Web Information Systems, Portugal (2008)
4. Koh, H.C., Tan, G.: Data mining applications in healthcare. Journal of Healthcare
Information Management 19(2), 64-72 (2005)
5. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in
databases. AI Magazine 17(3), 37-54 (1996)
6. Azevedo, A., Santos, M.F.: KDD, SEMMA and CRISP-DM: A parallel overview.
In: Proceedings of the IADIS European Conference on Data Mining, Netherlands,
pp. 182-185 (2008)
7. Paramasivam, V., Yee, T.S., Dhillon, S.K., Sidhu, A.S.: A methodological review of data
mining techniques in predictive medicine: An application in hemodynamic prediction for
abdominal aortic aneurysm disease. Biocybernetics and Biomedical Engineering 34(3),
139-145 (2014)
8. Hardin, J.M., Chhieng, D.C.: Data mining and clinical decision support systems.
In: Berner, E.S. (ed.) Clinical Decision Support Systems, pp. 44-63. Springer, USA (2007)
9. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.:
CRISP-DM 1.0 Step-by-step data mining guide. Technical report, SPSS (2000)
10. Barua, S., Islam, M. M., Murase, K.: ProWSyn: Proximity weighted synthetic
oversampling technique for imbalanced data set learning. In: Pei, J., Tseng, V.S., Cao, L.,
Motoda, H., Xu, G. (eds.) PAKDD 2013, Part II. LNCS, vol. 7819, pp. 317-328. Springer,
Heidelberg (2013)
11. Oracle. Oracle data mining concepts, http://docs.oracle.com/cd/
B28359_01/datamine.111/b28129/toc.htm
12. Kohavi, R., Provost, F.: Glossary of terms. Machine Learning - Special Issue on
Applications of Machine Learning and the Knowledge Discovery Process 30(2-3), 27-274
(1998)