Predicting Resurgery in Intensive Care - A data Mining Approach

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Abstract

Every day the surgical interventions are associated with medicine, and the area of critical care medicine is no exception. The goal of this work is to assist health professionals in predicting these interventions. Thus, when the Data Mining techniques are well applied it is possible, with the help of medical knowledge, to predict whether a particular patient should or not should be re-operated upon the same problem. In this study, some aspects, such as heart disease and age, and some data classes were built to improve the models created. In addition, several scenarios were created, with the objective can predict the resurgery patients. According the primary objective, the resurgery patients' prediction, the metric used was the sensitivity, obtaining an approximate result of 90%. Peer-review under responsibility of the Conference Program Chairs.

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APA

Peixoto, R., Ribeiro, L., Portela, F., Filipe Santos, M., & Rua, F. (2017). Predicting Resurgery in Intensive Care - A data Mining Approach. In Procedia Computer Science (Vol. 113, pp. 577–584). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.08.291

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