Generating a spatial coverage plan for the emergency medical service on a regional scale: Empirical versus random forest modelling approach

Dolejš, Martin; Purchard, Jan; Javorčák, Adam

doi:10.1016/j.jtrangeo.2020.102889

Cited by 12 publications

(13 citation statements)

References 34 publications

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“…McCormack and Coates [94], Park and Lee [86], Abensur et al [95], worked in the same line of Knight et al [96], including survival functions to capture the problem of addressing heterogeneous patient types.…”

Section: Modeling Issuesmentioning

confidence: 99%

“…Considering better computing capabilities available nowadays, many researchers include more features in the modeling process to obtain more realistic models. In this sense, it is important to highlight the inclusion of stochasticity in demand in [67,81,86,88,89,95,[113][114][115][116][117][118][119][120][121][122][123][124][125][126][127][128]. There are different ways to address stochasticity, but one of the prominent research areas is machine learning.…”

Section: Modeling Issuesmentioning

confidence: 99%

“…Some authors have considered objectives integration to address the problem as a multiobjective one. Among these studies are [76,82,88,95,110,136,141,144,145]. All of them used different ways to weigh the objective functions.…”

Section: Optimization Objectivesmentioning

confidence: 99%

“…In a novel application, game theory was used by Abensur et al [95] to select the best location for one emergency service provider in order to obtain better results in a competitive environment.…”

Section: Solution Approachesmentioning

confidence: 99%

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Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Neira-Rodado

Escobar

McClean³

2022

IJGI

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In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.

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Section: Modeling Issuesmentioning

confidence: 99%

Section: Modeling Issuesmentioning

confidence: 99%

Section: Optimization Objectivesmentioning

confidence: 99%

Section: Solution Approachesmentioning

confidence: 99%

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Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Neira-Rodado

Escobar

McClean³

2022

IJGI

View full text Add to dashboard Cite

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“…For example, the problems of space layout features and existence have been analyzed [8] and the spatial layout and functional differences of Hong Kong have been evaluated [9]. Regarding research themes, vulnerable groups [10], such as the elderly [11] and children [12], marginal areas such as poor countries [13] and villages [14][15][16], healthcare emergencies [17,18], and healthcare in response to disaster emergency services [19,20], as well as other special healthcare services in emergencies, are involved. For the above objects, some research has focuses on the diversified layout and capacity of healthcare facilities, and the opportunities for such groups and regions to obtain basic healthcare services under the condition of an unbalanced distribution of healthcare resources and multimodal transport of healthcare facilities [21,22].…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Spatial Match of Hierarchical Healthcare Facilities Considering Floating Population: A Case of Beijing, China

et al. 2022

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Good health and well-being are key to achieving the main goals of the UN Sustainable Development Goals (SDGs), especially after the outbreak of the COVID-19 epidemic. What is a concern for both government and society is how to understand the spatial match of hierarchical healthcare facilities and residential areas in terms of quantity and capacity, to meet the challenges of various diseases and build a healthy life. Using hierarchical healthcare data and cellphone signaling data in Beijing, China, we used the kernel density estimation, a bivariate spatial autocorrelation model, and a coupling index to explore the spatial relationships between hierarchical healthcare facilities and residential areas. We found large numbers of both healthcare facilities and residential areas in the urban center, and small numbers of both at the urban edge. The hospitals and designated retail pharmacies in the densely populated areas do not have enough capacity to meet the need of the population. In addition, the capacity of primary healthcare institutions can meet people’s needs. Our findings would serve as a reference for urban planning, optimization of hierarchical healthcare facilities, and research on similar themes.

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The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

Sahoh

Choksuriwong

2023

J Ambient Intell Human Comput

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A high-stakes event is an extreme risk with a low probability of occurring, but severe consequences (e.g., life-threatening conditions or economic collapse). The accompanying lack of information is a source of high-stress pressure and anxiety for emergency medical services authorities. Deciding on the best proactive plan and action in this environment is a complicated process, which calls for intelligent agents to automatically produce knowledge in the manner of human-like intelligence. Research in high-stakes decision-making systems has increasingly focused on eXplainable Artificial Intelligence (XAI), but recent developments in prediction systems give little prominence to explanations based on human-like intelligence. This work investigates XAI based on cause-and-effect interpretations for supporting high-stakes decisions. We review recent applications in the first aid and medical emergency fields based on three perspectives: available data, desirable knowledge, and the use of intelligence. We identify the limitations of recent AI, and discuss the potential of XAI for dealing with such limitations. We propose an architecture for high-stakes decision-making driven by XAI, and highlight likely future trends and directions.

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Generating a spatial coverage plan for the emergency medical service on a regional scale: Empirical versus random forest modelling approach

Cited by 12 publications

References 34 publications

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Multidimensional Spatial Match of Hierarchical Healthcare Facilities Considering Floating Population: A Case of Beijing, China

The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

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