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Article Dans Une Revue Mathematical and Computational Applications Année : 2021

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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Dates et versions

hal-03359993 , version 1 (30-09-2021)

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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