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

dc.contributor.author Rädsch, Tim
dc.contributor.author Reuter-Oppermann, Melanie
dc.contributor.author Richards, Dave
dc.date.accessioned 2020-12-24T19:20:15Z
dc.date.available 2020-12-24T19:20:15Z
dc.date.issued 2021-01-05
dc.description.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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2021.210
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70822
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Service Analytics
dc.subject decision support
dc.subject hems
dc.subject ml
dc.subject new zealand
dc.title Towards a Machine Learning-based Decision Support System for Dispatching Helicopters in New Zealand
prism.startingpage 1728
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