Predict Sepsis Level in Intensive Medicine – Data Mining Approach
作者： João M. C. Gonçalves , Filipe Portela , Manuel Filipe Santos , Álvaro Silva , José Machado
DOI: 10.1007/978-3-642-36981-0_19
关键词: Naive Bayes classifier 、 Intensive care unit 、 Set (abstract data type) 、 Confusion matrix 、 Support vector machine 、 Data mining 、 Supervised learning 、 Decision tree 、 Computer science 、 Intensive care
摘要: This paper aims to support doctor’s decision-making on predicting the Sepsis level. Thus, a set of Data Mining (DM) models were developed using prevision techniques and classification models. These enable better decision having into account level patient. The DM use real data collected from Intensive Care Unit Santo Antonio Hospital, in Oporto, Portugal. Classification considered predict sepsis supervised learning approach. induced making following algorithms: Decision Trees, Support Vector Machines Naive Bayes classifier. assessed Confusion Matrix, associated metrics, Cross-validation. analysis total error rate, sensitivity, specificity accuracy metrics used identify most relevant measures work demonstrates that it is possible with great
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