Table 1.
Score table of Glasgow coma scale (GCS) and simplified consciousness score (SCS).
Fig 1.
The flowchart for patient data extraction procedure.
Table 2.
Characteristics of each input variable from the patient data in the NTDB.
Table 3.
Performance of the machine learning algorithms for survivability predictions.
Table 4.
Comparison of false positive ratio (FPR) and true positive ratio (TPR) for machine learning algorithms for survivability predictions.
Fig 2.
ROC curves with AUC values for the neural networks and RTS.
Comparison between the neural network with simplified consciousness score (SCS), the neural network with Glasgow coma scale (GCS) and existing triage model, RTS. Neural networks developed without the injury severity score (ISS) outperformed the revised trauma score (RTS).
Table 5.
Comparison of the predicted survival score for the total dataset and each injury mechanism with the neural network.
Table 6.
Feature ranking of the machine learning algorithms; a lower number indicates a greater importance.
Table 7.
Performance of the machine learning algorithms for survivability predictions.
The standard errors were estimated by the jack-knife procedure.
Fig 3.
ROC curves with AUC values for the neural network and TRISS.
Comparison between the additionally developed neural network models with the injury severity score (ISS) and existing triage model, TRISS. In both model, with SCS and GCS, the neural network developed with the ISS also outperformed the trauma and injury severity score (TRISS).