Abstract
The nosocomial infections are a growing concern because they affect a large number of people and they increase the admission time in healthcare facilities. Additionally, its diagnosis is very tricky, requiring multiple medical exams. So, this work is focused on the development of a clinical decision support system to prevent these events from happening. The proposed solution is unique once it caters for the explicit treatment of incomplete, unknown, or even contradictory information under a logic programming basis, that to our knowledge is something that happens for the first time.
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Acknowledgements
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT (Fundação para a Ciência e Tecnologia) within the Project Scope: UID/CEC/00319/2013.
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Faria, R., Vicente, H., Abelha, A., Santos, M., Machado, J., Neves, J. (2016). A Case-Based Approach to Nosocomial Infection Detection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_14
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