Semi-automated initialization of simulations: an application to healthcare

Padilla, José J.; Diallo, Saikou Y.; Kavak, Hamdi; Sahin, Olcay; Sokolowski, John A.; Gore, Ross

doi:10.1177/1548512914565503

Cited by 3 publications

(1 citation statement)

References 13 publications

(11 reference statements)

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“…For example, in the first phase, the designing of the model, machine learning has been used to derive parameter values for agent-based models such as in cases of human mobility and obesity (e.g. Kavak 2007;Padilla et al 2016). Machine learning has also been used during the running of the model, often for agents to learn from past experiences and make more informed decisions via reinforcement learning or genetic algorithms or random forests (e.g.…”

Section: The Potential Of Machine Learning and Agent-based Modelingmentioning

confidence: 99%

Agent-Based Modeling and the City: A Gallery of Applications

et al. 2021

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Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.

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Section: The Potential Of Machine Learning and Agent-based Modelingmentioning

confidence: 99%

Agent-Based Modeling and the City: A Gallery of Applications

et al. 2021

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A Virtual Reality and Questionnaire Approach to Gathering Real-World Data for Agent-Based Crowd Simulation Models

Sinclair

Suwanwiwat

Lee

2019

PRESENCE: Virtual and Augmented Reality

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Gathering real world data is a crucial process in developing realistic agent-based crowd simulation models. In order to gather real world data, three types of data need to be considered: physical, mental and visual. Existing data gathering methods do not collect all three data types, but they provide a limited amount of data for agent-based simulations. This paper proposes using a combination of Virtual Reality and Questionnaires as a means to gathering real world data. This hybrid method collects all three data types and is validated by comparing it to data collected from the real world. Two data gathering experiments (real world and our proposed method) were conducted to collect all three types of data for comparison. Experimental results show that the proposed method can collect similar data to the real-world experiment, in particular for mental and visual data. Chi-Square Goodness of Fit Test proves that there is no significant difference between the real world and our proposed method for mental and visual data, whilst the test shows there is significant difference in physical data, in particular completed time. We propose an adjustment factor for the completed time data that mitigates the gap between virtual space and real space, and allows the results collected to be input into agent-based simulations as real-world data. Overall, the proposed method is cost effective, time efficient, reproducible, ecologically valid, and able to collect three types of data for agent-based crowd simulation models.

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A systematic literature review of simulation models for non-technical skill training in healthcare logistics

et al. 2018

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BackgroundResource allocation in patient care relies heavily on individual judgements of healthcare professionals. Such professionals perform coordinating functions by managing the timing and execution of a multitude of care processes for multiple patients. Based on advances in simulation, new technologies that could be used for establishing realistic representations have been developed. These simulations can be used to facilitate understanding of various situations, coordination training and education in logistics, decision-making processes, and design aspects of the healthcare system. However, no study in the literature has synthesized the types of simulations models available for non-technical skills training and coordination of care.MethodsA systematic literature review, following the PRISMA guidelines, was performed to identify simulation models that could be used for training individuals in operative logistical coordination that occurs on a daily basis. This article reviewed papers of simulation in healthcare logistics presented in the Web of Science Core Collections, ACM digital library, and JSTOR databases. We conducted a screening process to gather relevant papers as the knowledge foundation of our literature study. The screening process involved a query-based identification of papers and an assessment of relevance and quality.ResultsTwo hundred ninety-four papers met the inclusion criteria. The review showed that different types of simulation models can be used for constructing scenarios for addressing different types of problems, primarily for training and education sessions. The papers identified were classified according to their utilized paradigm and focus areas. (1) Discrete-event simulation in single-category and single-unit scenarios formed the most dominant approach to developing healthcare simulations and dominated all other categories by a large margin. (2) As we approached a systems perspective (cross-departmental and cross-institutional), discrete-event simulation became less popular and is complemented by system dynamics or hybrid modeling. (3) Agent-based simulations and participatory simulations have increased in absolute terms, but the share of these modeling techniques among all simulations in this field remains low.ConclusionsAn extensive study analyzing the literature on simulation in healthcare logistics indicates a growth in the number of examples demonstrating how simulation can be used in healthcare settings. Results show that the majority of studies create situations in which non-technical skills of managers, coordinators, and decision makers can be trained. However, more system-level and complex system-based approaches are limited and use methods other than discrete-event simulation.

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Semi-automated initialization of simulations: an application to healthcare

Cited by 3 publications

References 13 publications

Agent-Based Modeling and the City: A Gallery of Applications

Agent-Based Modeling and the City: A Gallery of Applications

A Virtual Reality and Questionnaire Approach to Gathering Real-World Data for Agent-Based Crowd Simulation Models

A systematic literature review of simulation models for non-technical skill training in healthcare logistics

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