Predicting Plateau Pressure in Intensive Medicine for Ventilated Patients
Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm H 2 O) in a real environment and using real data. The present study explored and assessed the possibility of predicting the Plateau pressure class with high accuracies. The dataset used only contained data provided by the ventilators. The best models are able to predict the Plateau Pressure with an accuracy ranging from 95.52% to 98.71%.
KeywordsBarotrauma Plateau Pressure Intensive Medicine Data Mining INTCare Mechanical Ventilation
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- 1.Santos, M., Azevedo, C.: Data Mining Descoberta do conhecimento em base de dados. FCA - Editora de Informática, Lda (2005)Google Scholar
- 2.Santos, M., Boa, M., Portela, F., Silva, Á., Rua, F.: Real-time prediction of organ failure and outcome in intensive medicine. In: 2010 5th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6 (2010)Google Scholar
- 3.Koh, H., Tan, G.: Data mining applications in healthcare. J. Healthc. Inf. Manag. 19(2), 64–72 (2005)Google Scholar
- 4.Anzueto, A., Frutos-Vivar, F., Esteban, A., Alía, I., Brochard, L., Stewart, T., Benito, S., Tobin, M.J., Elizalde, J., Palizas, F., David, C.M., Pimentel, J., González, M., Soto, L., D’Empaire, G., Pelosi, P.: Incidence, risk factors and outcome of barotrauma in mechanically ventilated patients. Intensive Care Med. 30(4), 612–619 (2004)CrossRefGoogle Scholar
- 9.Turban, E., Sharda, R., Delen, D.: Decision Support and Business Intelligence Systems, 9th edn. Prentice Hall (2011)Google Scholar
- 10.Torgo, L.: Data Mining with R: Learning with Case Studies. CRC Press - Taylor & Francis Group (2011)Google Scholar
- 11.Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F.: Misc Functions of the Department of Statistics (e1071) (2012)Google Scholar
- 12.Cortez, P.: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression (2013)Google Scholar
- 13.Witten, I., Frank, E., Hall, M.: Data Mining Pratical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann (2011)Google Scholar
- 14.Kantardzic, M.: Data Mining Concepts, Models, Methods, and Algorithms, 2nd edn. Wiley - IEEE Press (2011)Google Scholar
- 15.Ben-Hur, A., Weston, J.: A User’s Guide to Support Vector Machines. In: Carugo, O., Eisenhaber, F. (eds.) Data Mining Techniques for the Life Sciences. Humana Press (2010)Google Scholar
- 16.Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3rd edn. Morgan Kaufmann (2012)Google Scholar