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European Journal of Trauma and Emergency Surgery
Published in: European Journal of Trauma and Emergency Surgery 6/2017

01-12-2017 | Original Article

Survival prediction of trauma patients: a study on US National Trauma Data Bank

Authors: I. Sefrioui, R. Amadini, J. Mauro, A. El Fallahi, M. Gabbrielli

Published in: European Journal of Trauma and Emergency Surgery | Issue 6/2017

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Abstract

Background

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.

Methods

In this work we evaluate different approaches for predicting whether a patient will survive or not according to simple and easily measurable observations. We conducted a rigorous, comparative study based on the most important prediction techniques using real clinical data of the US National Trauma Data Bank.

Results

Empirical results show that well-known Machine Learning classifiers can outperform the TRISS methodology. Based on our findings, we can say that the best approach we evaluated is Random Forest: it has the best accuracy, the best area under the curve, and k-statistic, as well as the second-best sensitivity and specificity. It has also a good calibration curve. Furthermore, its performance monotonically increases as the dataset size grows, meaning that it can be very effective to exploit incoming knowledge. Considering the whole dataset, it is always better than TRISS. Finally, we implemented a new tool to compute the survival of victims. This will help medical practitioners to obtain a better accuracy than the TRISS tools.

Conclusion

Random Forests may be a good candidate solution for improving the predictions on survival upon the standard TRISS methodology.
Footnotes
2
In our case the learning rate was set to 0.3, the momentum to 0.2, the number of iterations to 500, and the hidden layer was composed of 9 nodes.
 
3
We used the 3.7.12 version of WEKA.
 
4
In WEKA this can be done with the “-M” option of the SMO classifier.
 
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Metadata
Title
Survival prediction of trauma patients: a study on US National Trauma Data Bank
Authors
I. Sefrioui
R. Amadini
J. Mauro
A. El Fallahi
M. Gabbrielli
Publication date
01-12-2017
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Trauma and Emergency Surgery / Issue 6/2017
Print ISSN: 1863-9933
Electronic ISSN: 1863-9941
DOI
https://doi.org/10.1007/s00068-016-0757-3

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