Abstract
The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94 %. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.
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Acknowledgments
This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013 and the contract PTDC/EEI-SII/1302/2012 (INTCare II).
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Braga, A. et al. (2016). Real-Time Models to Predict the Use of Vasopressors in Monitored Patients. In: Zheng, X., Zeng, D., Chen, H., Leischow, S. (eds) Smart Health. ICSH 2015. Lecture Notes in Computer Science(), vol 9545. Springer, Cham. https://doi.org/10.1007/978-3-319-29175-8_2
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DOI: https://doi.org/10.1007/978-3-319-29175-8_2
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