Abstract
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%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Santos, M., Azevedo, C.: Data Mining Descoberta do conhecimento em base de dados. FCA - Editora de Informática, Lda (2005)
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)
Koh, H., Tan, G.: Data mining applications in healthcare. J. Healthc. Inf. Manag. 19(2), 64–72 (2005)
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)
Al-Rawas, N., Banner, M.J., Euliano, N.R., Tams, C.G., Brown, J., Martin, A.D., Gabrielli, A.: Expiratory time constant for determinations of plateau pressure, respiratory system compliance, and total resistance. Crit. Care 17(1), R23 (2013)
Boussarsar, M., Thierry, G., Jaber, S., Roudot-Thoraval, F., Lemaire, F., Brochard, L.: Relationship between ventilatory settings and barotrauma in the acute respiratory distress syndrome. Intensive Care Med. 28(4), 406–413 (2002)
Gammon, R.B., Shin, M.S., Buchalter, S.E.: Pulmonary barotrauma in mechanical ventilation. Patterns and risk factors. Chest 102(2), 568–572 (1992)
Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Pervasive and intelligent decision support in intensive medicine – the complete picture. In: Bursa, M., Khuri, S., Renda, M.E. (eds.) ITBAM 2014. LNCS, vol. 8649, pp. 87–102. Springer, Heidelberg (2014)
Turban, E., Sharda, R., Delen, D.: Decision Support and Business Intelligence Systems, 9th edn. Prentice Hall (2011)
Torgo, L.: Data Mining with R: Learning with Case Studies. CRC Press - Taylor & Francis Group (2011)
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F.: Misc Functions of the Department of Statistics (e1071) (2012)
Cortez, P.: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression (2013)
Witten, I., Frank, E., Hall, M.: Data Mining Pratical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann (2011)
Kantardzic, M.: Data Mining Concepts, Models, Methods, and Algorithms, 2nd edn. Wiley - IEEE Press (2011)
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)
Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3rd edn. Morgan Kaufmann (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Oliveira, S. et al. (2015). Predicting Plateau Pressure in Intensive Medicine for Ventilated Patients. In: Rocha, A., Correia, A., Costanzo, S., Reis, L. (eds) New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-319-16528-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-16528-8_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16527-1
Online ISBN: 978-3-319-16528-8
eBook Packages: EngineeringEngineering (R0)