Abstract-At present, a company seeking to create competitive advantage, need to understand the data that is generated in the performance of their business. We intend to propose a framework that allows SMEs to take advantage of the information available, to adopt a set of measures supported by Business Intelligence (BI). The adoption of BI in Small and medium enterprises (SMEs) can create the need for organizations to adapt the processes that support systems for decision support, and may have to adapt their systems to the level of databases and applications, providing an additional perspective of information, enabling a more consistent analysis of the data in order to support the process of decision making.
Decision making assumes a critical role in the Intensive Medicine. Data Mining is emerging in the clinical area to provide processes and technologies for transforming data into useful knowledge to support clinical decision makers. Appling clustering techniques to the data available on the patients admitted into Intensive Care Units and knowing which ones correspond to readmissions, it is possible to create meaningful clusters that will represent the base characteristics of readmitted patients. Thus, exploring common characteristics it is possible to prevent discharges that will result into readmissions and then improve the patient outcome and reduce costs. Moreover, readmitted patients present greater difficulty to be recovered. In this work it was followed the Stability and Workload Index for Transfer (SWIFT). A subset of variables from SWIFT was combined with the results from laboratory exams, namely the Lactic Acid and the Leucocytes values, in order to create clusters to identify, in the moment of discharge, patients that probably will be readmitted.
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