Semantic-Rich Recommendation System for Medical Emergency Response System

Semantic-Rich Recommendation System for Medical Emergency Response System

R. Karthika, L. Jegatha Deborah, Wenying Zheng, Fayez Alqahtani, Amr Tolba, B. Gokula Krishnan, Ritika Bansal
Copyright: © 2024 |Pages: 18
DOI: 10.4018/IJSWIS.341231
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The emergency response process consists of methodical and coordinated series of actions and protocols executed by individuals and organizations to proficiently address crises. When planning for medical emergencies, it is vital to work with responsive medical organizations to ensure good communication and coordination. Unlike e-government processes, emergency response processes are focused on knowledge and may frequently change as the emergency situation develops. It is important to change the emergency response plan for dynamic situations and the proposed method helps to create a flexible plan for emergency responses. The proposed approach uses a system for organizing knowledge to figure out the needs and the resources essential for an emergency. It helps to identify the organizations to be involved based on their rules for mutual aid and jurisdiction. Experimental analysis shows that the proposed method outperforms Smart-c and DCERP in suggesting a greater number of hospitals during medical emergency and achieves 0.8, 0.9 and 0.9 precision, recall, and f-measure approximately.
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An emergency refers to a circumstance that presents an imminent peril to human well-being, survival, and assets, necessitating prompt measures to avert its exacerbation. The interventions are structured as a procedural framework, commonly referred to as an emergency response procedure. This procedure is typically outlined in an emergency plan (Liu et al., 2015). The emergency response plan serves the purpose of delineating the necessary courses of action to be undertaken to effectively manage crisis situations. Additionally, it furnishes vital details pertaining to the many organizations and resources that are engaged in the emergency response process (Ni et al., 2020). The premise underlying this concept is that the process of emergency response bears resemblance to that of a business, thereby rendering it amenable to being represented as a workflow model. A workflow can be characterized as a visual depiction of a specific process, including clearly defined operations, commonly known as tasks.

An emergency response refers to the coordinated efforts and actions taken in a crisis situation. It involves various activities that are carefully planned and executed to address the emergency effectively. The key to successful emergency planning is ensuring clear communication and collaboration among different operational systems. Agencies from both the public and commercial sectors, as well as government agencies, are involved in this cooperative effort. Government agencies typically rely on public sector processes, such as e-government, to facilitate their emergency response activities. On the other hand, nongovernment agencies often use private sector processes, such as e-commerce, to support their efforts. The emergency response workflow is designed to accommodate both predictable and nondeterministic elements, allowing for flexibility in adapting to different environments (Elahraf et al., 2022).

The dynamic workflow structure of an emergency response emerges based on various factors. Situational considerations include user choices, available resources, and response organization guidelines and regulations. The operational system follows jurisdictional rules that determine the responsibilities of local agencies in extending resources during emergencies. Public and private organizations can work together under the same or separate jurisdictions as a result of mutual aid agreement regulations. Before proceeding with the emergency situation, organizations should determine whether the emergency news is real or not. Zhang et al (2023) introduced a rapid fake news detection model for cyber-physical social services that employs deep learning techniques.

A growing need has emerged for ontologies to be interoperable with each other to obtain precise information. The presence of ontological heterogeneity further complicates the process of achieving interoperability. Mani and Annadurai (2022) introduced a novel revised framework for propagating similarity in ontology mapping. Tiwari and Garg (2022) discussed the methodology used to assess the quality of ontologies. This research will promote the use of existing ontologies and enhance the compatibility of semantic systems. Ontology and semantic web rules language (SWRL) are used to represent data, allowing for easier sharing and interoperability between various information systems. Because of this facility, timely answers to crises can be crafted on the go. The appropriate actions, resources, and organizations for responding are determined through the use of ontology-based reasoning. Numerous online services that expose the features of operational systems used by response agencies and resource provider organizations enable the execution of each task within the emergency response plan. Specifically, for resource management functions, the crisis management system can communicate with response organizations through the response process created using their respective web service application programming interfaces (APIs) (Lemos et al., 2015).

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