Novel approach for automating medical emergency protocol in military environment.

BACKGROUND AND OBJECTIVES: Categorization of the casualties in accordance with medical care priorities is crucial in a military environment. Automation of the triage process is still a challenging task. The goal of the paper is to propose a novel algorithm for automation of medical emergency protocol in the military environment by the creation of classifiers that can provide accurate prioritization of injured soldier cases. It is a part of a complex military telemedicine system that provides continuous monitoring of soldiers' vital data gathered on-site using an unobtrusive set of sensors.

METHODS: After pre-processing the collected raw physiological data and eliminating the outliers using Naïve Bayesian Classifier, the system is capable of calculating the risk level and categorizing the victims based on Markov Decision Process. The NBC has been trained with a dataset that has contained labels and 6 features. Training set has held 8000 randomly chosen samples. Twenty percent of the determined dataset has been used for the validation set.

RESULTS: For algorithm verification, several evaluation scenarios have been created. In each scenario, randomly generated vital sign data describing the hypothetical health condition of soldiers was contemporarily assessed by the system as well as by 50 experienced military medical physicians.

CONCLUSION: The obtained correlation result of the proposed algorithm and medical physicians' classifications is strong evidence that the system can be implemented in warfare emergency medicine.