Towards a Machine Learning-based Decision Support System for Dispatching Helicopters in New Zealand

Date
2021-01-05
Authors
Rädsch, Tim
Reuter-Oppermann, Melanie
Richards, Dave
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Abstract
Helicopters play an important role in emergency medical service systems worldwide. In sparsely populated countries like New Zealand with long distances between hospitals, helicopters are often the best way to help critically injured patients. As helicopters are extremely costly, they should only be dispatched when really necessary. In this paper, we use data from the South Island of New Zealand to test several Machine Learning approaches and show that they can be used to support dispatchers by identifying emergencies likely to require a helicopter response. We follow a non-static dataset, as the information is successively available during an emergency, and demonstrate that even a limited approach, based only on geographic incident information, can yield an Average Precision of 94% for highlighting critical emergencies. In the latter parts of this paper, we investigate different compositions of training data to assess the impact of a potential concept drift.
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Service Analytics, decision support, hems, ml, new zealand
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