The Regional Characteristics of 119 Ambulance Dispatch, the Distance and Response Time to the Scene

Kyoung-youl Lee; Jun-dong Moon; Eun-sook Choi

doi:10.5392/jkca.2016.16.01.482

The Regional Characteristics of 119 Ambulance Dispatch, the Distance and Response Time to the Scene

The Journal of the Korea Contents Association ◽

10.5392/jkca.2016.16.01.482 ◽

2016 ◽

Vol 16 (1) ◽

pp. 482-492 ◽

Cited By ~ 1

Author(s):

Kyoung-youl Lee ◽

Jun-dong Moon ◽

Eun-sook Choi

Keyword(s):

Response Time ◽

Regional Characteristics ◽

Ambulance Dispatch

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Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Mathematical and Computational Applications ◽

10.3390/mca26030056 ◽

2021 ◽

Vol 26 (3) ◽

pp. 56

Author(s):

Héber H. Arcolezi ◽

Selene Cerna ◽

Christophe Guyeux ◽

Jean-François Couchot

Keyword(s):

Response Time ◽

Rural Areas ◽

Differential Privacy ◽

Sensitive Data ◽

Rescue Service ◽

Location Data ◽

Ambulance Dispatch ◽

Considerable Impact ◽

The Moment ◽

Ambulatory Services

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 sensitive data because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.

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Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000339 ◽

2016 ◽

Vol 63 (6) ◽

pp. 333-342

Author(s):

Roberto Limongi ◽

Angélica M. Silva

Keyword(s):

Response Time ◽

Short Term Memory ◽

Estimation Error ◽

Predictive Coding ◽

Time Estimation ◽

Prediction Errors ◽

Memory Scanning ◽

Short Term ◽

Term Memory ◽

Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

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