METHOD AND APPARATUS FOR AUTOMATED PATIENT SEVERITY RANKING IN MASS CASUALTY INCIDENTS
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May 8, 2015
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May 8, 2015
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A single disaster could leave hundred thousands of people injured or even dead. In order for the rescue team to reach this knowledge they should send one of the team members to investigate the scene. Body Sensor Networks are emerging systems that can be easily used to measure patient's physiological data and communicate relevant data to other patient using their smart phones. We propose to use Bloom filters, a space efficient probabilistic data structure, for efficiently collecting the dynamic status of patients in a mass casualty scenario. The collected data is disseminated to all nodes in the network to make it available to the rescue team wherever they arrive. In particular, we show that the members of the most urgent cases found at each node are found to be very close to the set of the actual urgent cases.
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