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Confusion Matrix, including associated metrics, and Cross-validation were used to evaluate the models. Associated metrics used to identify the most relevant measures to predict sepsis level and treatment procedures include the analysis of the total error rate, sensitivity, specificity, and accuracy measures. The data used in DM models were collected at the Intensive Care Unit of the Centro Hospitalar do Porto, in Oporto, Portugal. Encapsulated within a supervised learning context, classification models were applied to predict sepsis level and direct the therapeutic plan for patients with sepsis. This work concludes that it was possible to predict sepsis level (2nd and 3rd) with great accuracy (accuracy: 100%), but not for the therapeutic plan (best accuracy level: 62.8%).<\/p>","DOI":"10.4018\/ijhisi.2014070103","type":"journal-article","created":{"date-parts":[[2014,12,9]],"date-time":"2014-12-09T13:04:18Z","timestamp":1418130258000},"page":"36-54","source":"Crossref","is-referenced-by-count":4,"title":["Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine"],"prefix":"10.4018","volume":"9","author":[{"given":"Jo\u00e3o M. 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