Accurate and effective diagnosis of actual injury severity can be problematic in 
trauma patients. Inherent physiologic compensatory mechanisms may prevent 
accurate diagnosis and mask true severity in many circumstances. The objective 
of this project was the development and validation of a multiparameter machine 
learning algorithm and system capable of predicting the need for life-saving 
interventions (LSIs) in trauma patients. Statistics based on means, slopes, and 
maxima of various vital sign measurements corresponding to 79 trauma patient 
records generated over 110,000 feature sets, which were used to develop, train, 
and implement the system. Comparisons among several machine learning models 
proved that a multilayer perceptron would best implement the algorithm in a 
hybrid system consisting of a machine learning component and basic detection 
rules. Additionally, 295,994 feature sets from 82 h of trauma patient data 
showed that the system can obtain 89.8 % accuracy within 5 min of recorded LSIs. 
Use of machine learning technologies combined with basic detection rules 
provides a potential approach for accurately assessing the need for LSIs in 
trauma patients. The performance of this system demonstrates that machine 
learning technology can be implemented in a real-time fashion and potentially 
used in a critical care environment.