Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time
Résumé
Emergency medical services (EMS) provide crucial emergency assistance
and ambulatory services. One key measurement of EMS’s quality of service
is their ambulances’ response time (ART), which generally refers to the
period between EMS notification and the moment an ambulance arrives on
the scene. Due to many victims requiring care within adequate time
(e.g., cardiac arrest), improving ARTs is vital. This paper proposes to
predict ARTs using machine-learning (ML) techniques, which could be used
as a decision-support system by EMS to allow a dynamic selection of
ambulance dispatch centers. However, one well-known predictor of ART is
the location of the emergency (e.g., if it is urban or rural areas),
which is
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Soumis le : jeudi 30 septembre 2021-15:32:22
Dernière modification le : mercredi 19 juillet 2023-12:29:52
Archivage à long terme le : vendredi 31 décembre 2021-20:42:12
Dates et versions
Identifiants
- HAL Id : hal-03359993 , version 1
Citer
Héber Hwang Arcolezi, Selene Leya Cerna Nahuis, Christophe Guyeux, Jean-François Couchot. Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time. Mathematical and Computational Applications, 2021, 26 (3), pp.56 (17). ⟨hal-03359993⟩
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