Survival prediction of trauma patients: a study on US National Trauma Data Bank
- 22 February 2017
- journal article
- research article
- Published by Springer Nature in European Journal of Trauma and Emergency Surgery
- Vol. 43 (6) , 805-822
Abstract
Exceptional circumstances like major incidents or natural disasters may cause a huge number of victims that might not be immediately and simultaneously saved. In these cases it is important to define priorities avoiding to waste time and resources for not savable victims. Trauma and Injury Severity Score (TRISS) methodology is the well-known and standard system usually used by practitioners to predict the survival probability of trauma patients. However, practitioners have noted that the accuracy of TRISS predictions is unacceptable especially for severely injured patients. Thus, alternative methods should be proposed.Keywords
This publication has 40 references indexed in Scilit:
- A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining TechniquesHealthcare Informatics Research, 2011
- Use of an artificial neural network to predict head injury outcomeJournal of Neurosurgery, 2010
- A concept for major incident triage: full-scaled simulation feasibility studyBMC Emergency Medicine, 2010
- A survey of cross-validation procedures for model selectionStatistics Surveys, 2010
- The WEKA data mining softwareACM SIGKDD Explorations Newsletter, 2009
- Comparison of Classification Methods Based on the Type of Attributes and Sample SizeJournal of Convergence Information Technology, 2009
- Top 10 algorithms in data miningKnowledge and Information Systems, 2007
- Incorporating Recent Advances To Make the TRISS Approach Universally Available2006
- Logistic Regression2005
- Improvements to Platt's SMO Algorithm for SVM Classifier DesignNeural Computation, 2001