Discrete Event Models of Medical Emergencies

Discrete Event Models of Medical Emergencies

Calin Ciufudean, Otilia Ciufudean
Copyright: © 2015 |Pages: 10
DOI: 10.4018/978-1-4666-5888-2.ch341
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As modern society consists of large social networks there is a lot of places where infectious diseases are easily spreading, and therefore these processes have been widely studied using different approaches as did Shah and Zaman (2010), (2011), and Lappas et al. (2010).

An Artificial Social System (ASoS) is a set of restrictions on agent’s behavior in a multi-agent environment (Moses & Tennenholtz, 2002). ASoS allows agents to coexist in a shared environment and pursue their respective goals in the presence of other agents. A plan (Evans,Gor, & Unger, 1996) is said to guarantee the attainment of a particular goal starting from a particular initial state. In controlling the actions, or strategies, available to an agent, the social law plays a dual role. By reducing the set of strategies available to a given agent, the social system may limit the number of goals the agent is able to attain. By restricting the behavior of the other agents, however, the social system may make it possible for the agent to attain more goals and in some cases these goals will be attainable using more efficient plans than in the absence of the social system. A semantic definition of artificial social systems gives us the ability to reason about such systems. In order to be able to reason properly, we need a mathematical model and a description language. Different approaches were proposed for modeling these tasks in order to minimize the mentioned time: (Shah & Zaman, 2010; Anderson & May, 1991; Gaspar, 1991; Balloni, 2004; Ministerio da Ciencia, 2011). The proposed methods concern mostly drastic administrative duties which cannot be generalized as well as a versatile model like ASoS. As we see in different organizations, also in hospitals, informational technologies utilization on different levels varies and depends among other things, on the intelligence of hospital management. It is well known that in these processes are involved many variables and one hardly finds a universal pattern to all possible situations, data basis (even huge ones) will provide only partial solutions. In this work we address a new approach based on artificial social systems modeled with timed Petri nets. The novelty of our approach resides not only in stating the theoretical support for it, but also in the involvement of this tool for saving human lives. Discrete event formalisms are addressed to efficiently solve this problem. The content of the article is as follows: section 2 introduces a class of Petri nets for modeling artificial social systems; section 3 illustrates the concepts discussed in the previous article, section 4 frames the cyclic time of Petri nets which model artificial social systems for medical emergency scheduling; section 5 proposes an algorithm for verifying the optimum cyclic time of an ASoS model, and section 6 concludes the approaches given in this work and also suggests some possible research development.

Key Terms in this Chapter

Artificial Social Laws: The set of specific functional properties of an artificial social system.

Cyclic Time: The periodic initialization of a process.

Hospital Emergency: A hospital department (ED), also known as accident & emergency (A&E).

Multi-Agent Systems: A computerized system composed of multiple interacting intelligent agents within an environment.

Petri Nets: A mathematical modeling language for the description of distributed systems.

Artificial Social Systems: The set of protocols and interactions of intelligent machines with each other and with humans in equal measure.

Diagnosis: The identification of the nature and cause of a certain disease.

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