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%.

[1]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[2]  Ian H. Witten,et al.  Chapter 1 – What's It All About? , 2011 .

[3]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[4]  Jian Pei,et al.  2012- Data Mining. Concepts and Techniques, 3rd Edition.pdf , 2012 .

[5]  Mehmed Kantardzic,et al.  Data Mining: Concepts, Models, Methods, and Algorithms , 2002 .

[6]  Manuel Filipe Santos,et al.  Preâmbulo [a] "Data mining: descoberta de conhecimento em bases de dados" , 2005 .

[7]  Carl G. Tams,et al.  Expiratory time constant for determinations of plateau pressure, respiratory system compliance, and total resistance , 2013, Critical Care.

[8]  Luís Torgo,et al.  Data Mining with R: Learning with Case Studies , 2010 .

[9]  Kurt Hornik,et al.  Misc Functions of the Department of Statistics (e1071), TU Wien , 2014 .

[10]  D. Edwards Data Mining: Concepts, Models, Methods, and Algorithms , 2003 .

[11]  S. Buchalter,et al.  Pulmonary barotrauma in mechanical ventilation. Patterns and risk factors. , 1992, Chest.

[12]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[13]  Salvador Benito,et al.  Incidence, risk factors and outcome of barotrauma in mechanically ventilated patients , 2004, Intensive Care Medicine.

[14]  Filipe Portela,et al.  Real-time prediction of organ failure and outcome in intensive medicine , 2010, 5th Iberian Conference on Information Systems and Technologies.

[15]  François Lemaire,et al.  Relationship between ventilatory settings and barotrauma in the acute respiratory distress syndrome , 2002, Intensive Care Medicine.

[16]  Efraim Turban,et al.  Decision Support and Business Intelligence Systems (8th Edition) , 2006 .

[17]  H. Koh,et al.  Data mining applications in healthcare. , 2005, Journal of healthcare information management : JHIM.

[18]  Filipe Portela,et al.  Pervasive and Intelligent Decision Support in Intensive Medicine - The Complete Picture , 2014, ITBAM.